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Amazon Research Scientist Interview Guide

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Getting ready for an Research Scientist interview at Amazon? The Amazon Research Scientist interview span across 10 to 12 different question topics. In preparing for the interview:

• Know what skills are necessary for Amazon Research Scientist roles.
• Gain insights into the Research Scientist interview process at Amazon.
• Practice real Amazon Research Scientist interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Amazon Research Scientist interview.

Amazon Research Scientist Salary

Average Base Salary

Average Total Compensation

View the full Research Scientist at Amazon salary guide

Amazon Research Scientist Interview Process

Typically, interviews at Amazon vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

We've gathered this data from parsing thousands of interview experiences sourced from members.

Amazon Research Scientist Interview Questions

Practice for the Amazon Research Scientist interview with these recently asked interview questions.

Amazon Research Scientist Jobs

• Automated reasoning
• Cloud and systems
• Computer vision
• Conversational AI
• Information and knowledge management
• Machine learning
• Operations research and optimization
• Quantum technologies
• Search and information retrieval
• Security, privacy, and abuse prevention
• Sustainability
• Publications
• Conferences
• Code and datasets
• Alexa Prize
• Academics at Amazon
• Amazon Research Awards
• Research collaborations

How to build a successful career as a scientist at Amazon

Belinda zeng, head of applied science and engineering at amazon search science and ai, shares her perspective..

Editor’s note: Belinda Zeng joined Amazon in 2017 as the global head of data science and has participated in hundreds of interviews for science roles across the company. Here she shares her thoughts on what it takes to succeed as a scientist at Amazon.

I have had the pleasure of working at Amazon as a science leader for the past four-plus years. Two years ago I became what is known in Amazon as a Bar Raiser. Bar Raisers are experienced interviewers who help to raise the Amazon recruiting standard. I lead a science and engineering team called M5 — the five Ms stand for multi-lingual, multi-locale, multi-modal, multi-task, multi-entity — a large-scale AI program focused on transforming how deep learning models are built and deployed at Amazon. My team innovates to help bring Amazon services beyond the current state of the art, achieve step function improvement, and unlock many new downstream applications in search, advertising, and catalog, to name just a few.

Looking back on my journey at Amazon, and drawing on my experience as a Bar Raiser, I’d like to share some information and advice with those who are interested in exploring opportunities with Amazon.

What does the hiring team look for?

I still remember the day when I submitted my application to Amazon, wondering what the hiring team was seeking. Four years later, I know the answer to that question.

First and foremost are the functional competencies, including science breadth, depth, experience in developing science applications, and scripting language coding skills. There are a number of science roles within Amazon and because the core responsibilities for those roles are distinct, the required technical skills differ.

Data scientists, for example, are considered as generalists who investigate the feasibility of applying scientific principles to business problems. They are normally assessed for data skills, math/stats knowledge and, most likely, analytical mindset, and business acumen.

Research and applied scientists are expected to have deep expertise in one of the data-driven science disciplines and to apply scientific principles to support significant invention. The hiring team typically delves into one or two scientific areas such as machine learning, speech recognition, operations research, and robotics.

Development of software code is a core skill expected from applied scientists as they are deeply involved in bringing their algorithms to production. Economists are vetted for their experience developing offline code for applied econometric applications. The second area we assess is how well applicants can apply the Amazon Leadership Principles. In the more than 200 loops (Amazon’s name for our interview process) in which I have participated, three Leadership Principles stand out for scientists:

• Learn and Be Curious : In my interview conversations, I look for data points that show the candidate proactively seeking opportunities to learn new skills and improve themselves versus staying with familiar situations or avoiding new experiences.
• Dive Deep : I look for those who investigate and get details to solve a problem, even when faced with challenges, as opposed to having only a surface-level understanding of projects;
• Invent and Simplify : I look for those who generate new ideas or simplify a solution for long-term wins versus creating a cumbersome process to solve a short-term problem.

For senior level roles, a writing exercise is normally required as well. Amazon uses written documents to communicate ideas and influence others. We look for candidates who are able to articulate a process, product or point of view in a clear, crisp, and logical manner.

During the interview debrief, we often debate whether a candidate “raises the bar”. A bar-raising candidate is a candidate who is better qualified than 50% of existing employees at the same level. For entry level roles, it means the ability to fulfill a task with supervision. For experienced hires, it means to deliver with autonomy and minimum supervision.

How does Amazon support its scientists?

For scientists hired by Amazon, there are many types of career support available from both your team and the company.

Learning : Amazon seeks candidates who are passionate lifetime learners, and provides numerous opportunities to support that instinct. That can come in the form of online and classroom courses, team wiki and learning portals, as well as access to experts and mentorship. For example, 200 Amazon scientists were randomly selected to participate in a Coursera beta program to take free online courses for six months. The scientists were able to stay current in their science specialty and increase their skills and knowledge to apply on their job.

In addition, there is a special program called the Day 1 Science Mentorship Program. That program pairs up new-hire scientists with experienced Amazon science leaders to ease the transition into Amazon’s business culture.

Community connection : An expansive community is critical to a scientist’s development. At Amazon, there are hundreds of science-focused meetings, reading clubs, invited talk series, and workshops happening on a regular basis. These mechanisms not only offer the opportunity to connect with people who have similar research interests, but also provide a forum to showcase innovative work.

The company also holds multiple annual science conferences for Amazonians interested in innovative science. One is the annual Amazon Machine Learning Conference, a four-day event that covers most major areas in machine learning and attracts thousands of attendees and submissions. Collectively we continually raise the scientific bar at Amazon.

Growth : At Amazon, we all grow with the company. There are ample opportunities to stretch yourself, by expanding your scope and growing your skill set. I have helped scientists on my team transition into different science roles; relocate internationally for a stretching assignment; and watched some go from individual contributors to tech leads and eventually managerial positions.

How do you build a successful career at Amazon?

Here are some insights from my personal experience:

Trust is a multiplier. There are multiple meanings inside this single word: transparency, integrity, capability, and many more. For scientist roles, trust naturally expands with competency — stay fresh, relevant and capable — and contribution, which means producing high quality, timely results. I have worked with many great scientists and observed how they build trust through capability and results, which in turn brought greater influence. A common pitfall is sometimes we tend “assume” trust by overestimating our capabilities. Consistently asking for feedback, then listening to and acting on that feedback will help close that gap and build trust.

Work backwards from a problem. New scientist hires, especially those who recently moved from a foundational research role, sometimes find it hard to transition into the Amazon working backwards culture. The goals in foundational research are to generate knowledge or understanding regarding a particular phenomenon, without much focus on real-world impact. However, for applied research at Amazon, the main criterion of success lies in how well findings can be used to have a positive impact on customers. A well-balanced focus between curiosity- and solution-driven research is key to ensure effective execution.

Be a well-rounded scientist. Being a scientist means more than running experiments. Scientists are expected to understand the business problem, decompose a complex issue into components that are addressable by science, and communicate science effectively. Success is the journey, not the destination. If you are interested in joining Amazon’s customer-obsessed journey, please visit the Amazon Science careers page . It is always Day 1 at Amazon.

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Amazon Research Scientist Interview Guide

Preparing for an Amazon Research Scientist interview requires thorough knowledge and strategic preparation. The Amazon Research Scientist interview is a highly sought-after opportunity for those aspiring to work in cutting-edge research and development.

In this article, we will provide valuable insights and guidance to help you navigate the intricacies of an Amazon Research Scientist interview. From understanding the interview process to mastering the technical and behavioral aspects, we will equip you with the essential tools to excel in the Amazon Research Scientist interview and secure your dream role.

Understanding the Amazon Research Scientist Role

The role of a research scientist at amazon:.

The role of a Research Scientist at Amazon is crucial in driving innovation and advancing scientific knowledge. Research Scientists at Amazon are responsible for conducting groundbreaking research, developing novel algorithms, and applying scientific methodologies to solve complex problems. They work on a wide range of projects, spanning various domains such as machine learning, computer vision, natural language processing, and robotics.

These scientists collaborate with interdisciplinary teams, including engineers, product managers, and business stakeholders, to translate their research findings into practical solutions and deliver impactful products and services. Additionally, Research Scientists at Amazon are expected to stay up-to-date with the latest advancements in their field, publish their research findings, and contribute to the broader scientific community. With access to vast amounts of data, cutting-edge technologies, and a culture of innovation, Research Scientists at Amazon play a pivotal role in shaping the future of technology and improving customer experiences.

Key Responsibilities of an Amazon Research Scientist:

Research scientists at Amazon are expected to:

• Collaborate with cross-functional teams to identify research objectives and prioritize projects.
• Design and conduct experiments to test hypotheses and analyze data.
• Develop and implement algorithms and models to solve business problems.
• Leverage machine learning and artificial intelligence techniques to drive innovation.
• Stay up-to-date with the latest research trends and technologies in relevant domains.
• Communicate findings and insights effectively to stakeholders.

The Amazon Research Scientist Interview Process

The Amazon Research Scientist Interview Process is a rigorous and comprehensive evaluation designed to identify exceptional individuals with a strong scientific background and research expertise. The process typically consists of multiple stages, including initial screening, technical assessments, and onsite interviews. Candidates are assessed on various criteria, such as problem-solving skills, research acumen, data analysis proficiency, and leadership potential. The interviews often involve technical questions, hypothetical scenarios, and discussions on research projects. Successful candidates demonstrate not only a deep understanding of their field but also the ability to think critically, collaborate effectively, and deliver innovative solutions. Amazon’s research scientist interview process aims to identify top talent who can contribute to cutting-edge research and drive impactful advancements in their respective fields.

Stage 1: Initial Screening

The Amazon Research Scientist Interview process typically begins with an initial screening of the candidate’s application materials, including their resume and cover letter. It is crucial to showcase relevant experience, academic achievements, and technical expertise in these documents to capture the attention of the hiring team.

Stage 2: Technical Phone Interview

If the initial screening is successful, candidates move on to the technical phone interview. This stage involves a conversation with an Amazon research scientist or engineer who assesses the candidate’s technical knowledge, problem-solving skills, and ability to apply scientific principles to real-world scenarios. To excel in this interview, it is vital to prepare by reviewing fundamental concepts and practicing coding problems.

Stage 3: On-Site Interviews

The next stage of the Amazon Research Scientist Interview process is the on-site interviews. This phase typically consists of multiple rounds, each focusing on different aspects of the candidate’s abilities. The interviews may include technical, behavioral, and case study components.

Technical Interviews:-

Technical interviews assess the candidate’s proficiency in areas such as machine learning, data analysis, algorithms, and coding. Be prepared to showcase your expertise by explaining concepts clearly, solving complex problems, and discussing your research experience.

Behavioral Interviews:-

Behavioral interviews aim to evaluate the candidate’s interpersonal skills, teamwork abilities, and alignment with Amazon’s leadership principles. Prepare examples that highlight your collaboration, problem-solving, and communication skills in previous projects or research endeavors.

Case Study Interviews:-

Case study interviews present candidates with real-world scenarios and assess their ability to analyze problems, propose solutions, and demonstrate their understanding of Amazon’s business and customer-centric approach.

Stage 4: Leadership Principles Assessment

Throughout the interview process, Amazon emphasizes its leadership principles, which are a set of fundamental values that guide decision-making and behaviors within the company. Candidates may encounter questions aimed at assessing their alignment with these principles. Familiarize yourself with Amazon’s leadership principles and ensure you can provide concrete examples of how you embody them.

Tips for Success in the Amazon Research Scientist Interview

To enhance your chances of success in the Amazon Research Scientist Interview, consider the following tips:

• Thoroughly research Amazon’s research initiatives, projects, and recent publications to gain a deeper understanding of the company’s focus areas and priorities.
• Review fundamental concepts, algorithms, and machine learning techniques to strengthen your technical knowledge.
• Practice coding problems and algorithmic puzzles to sharpen your problem-solving skills.
• Prepare concrete examples that demonstrate how you have applied scientific methods, collaborated effectively, and delivered results in your previous research or work experiences.
• Develop concise and compelling stories that highlight your alignment with Amazon’s leadership principles.
• Practice mock interviews with peers or mentors to gain confidence and receive feedback on your performance.

Conclusion:

The Amazon Research Scientist Interview is a gateway to an exciting and challenging career in research and innovation. By understanding the expectations, stages, and key principles of the interview process, you can better prepare yourself for success. Remember to showcase your technical expertise, problem-solving abilities, and alignment with Amazon’s leadership principles. With thorough preparation and a confident approach, you can increase your chances of securing a position as a research scientist at Amazon and contribute to the company’s groundbreaking advancements.

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Amazon data scientist interview (questions, process, prep)

Data scientist interviews at Amazon are challenging. The questions are difficult, specific to Amazon, and cover a wide range of topics.

The good news is that the right preparation can help you maximize your chances of landing a job offer at Amazon (or AWS). We’ve analyzed 150+ data scientist interview questions reported by real Amazon candidates, categorized them, and listed examples below. 

Read on for our ultimate guide for success, including practice questions, links to helpful resources, and preparation tips to help you land that Amazon data scientist role.

Here's an overview of what we'll cover:

• Process and timeline
• Machine learning
• Behavioral (leadership principles)
• Preparation tips

Click here to practice 1-on-1 with a data science ex-interviewer

1. interview process and timeline, 1.1 what interviews to expect.

What's the Amazon data scientist interview process and timeline ? 

It typically takes four to eight weeks and follows the steps below. If you're interviewing at AWS, you can expect a similar timeline.

• Resume screen
• Recruiter phone screen (~30 min)
• Technical screen (1 or 2 screens, ~60 min each)
• Onsite interviews (5-6 interviews, 45-60 min each)

1.1.1 Resume screen

First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage.

If you’re looking for expert feedback on your resume, you can get input from our  team of ex-Amazon recruiters , who will  cover what achievements to focus on (or ignore), how to fine tune your bullet points, and more.

Of course, if you have any connections to current Amazon employees, consider asking for a referral. This often helps candidates get their feet in the door. 

1.1.2 Recruiter screen

Once you’ve applied or been directly contacted by a recruiter, the hiring process generally starts with a brief recruiter screen call. This will be a discussion of your background as well as the interviews ahead of you. Prepare answers to simple behavioral questions (see section 2.4) to show why you’re a good fit for Amazon. 

You may be speaking directly with your recruiter or with your hiring manager. This may not be someone with a technical background. If your recruiter hasn’t already detailed the process, this is a good time to ask specific questions about what to expect and what to prepare, as the process may differ per role.

Note: some candidates who have been contacted directly by recruiters via LinkedIn may skip the initial call and pass directly to the technical screen(s).

1.1.3 Technical screen

As mentioned above, the Amazon data scientist interview process differs between roles, so there are a few possibilities for your technical screen. You may have a take home assignment , a video call with live coding, a call focused on machine learning, or a combination of two of these.

While role-specific exercises or online assessments appear to be more common for internships, they are required for the occasional experienced role as well. They will consist of a coding assessment or a case study for you to explore in-depth. You may be asked to present your case study as a second stage of your technical screen, or during one of the onsite interview rounds.

Otherwise, your recruiter will schedule one or two interviews using Amazon Chime. Come prepared to answer machine learning questions and to work out SQL and Python/R questions on a shared notepad document. While other companies like Google or Facebook focus solely on technical skills at this step in the process, Amazon is equally interested in your past experience. Be ready to explain your past projects and business issues that you’ve solved, detailing concrete steps and framing them in the context of the leadership principles . 

1.1.4 Onsite interviews

If you crack the technical screen(s), the next step is to spend a full day onsite at the Amazon offices doing five or six separate rounds, one of which will take the form of an informal lunch interview. Due to COVID-19, this may happen as a “ virtual onsite ” using Amazon Chime.

These interviews will last 45 to 60mins and will be one-on-ones with a mix of people from the team you’re applying to join, including peers, the hiring manager, and a senior executive called the Bar Raiser.

Bar Raisers are not associated with the team you’re applying for. Instead, they focus on overall candidate quality rather than specific team needs. They get special training to make sure Amazon’s hiring standards stay high, so they are a big barrier between you and the job offer.

The format of the interviews differ, but may consist of case studies, technical presentations , Q&As, whiteboarding, or otherwise. Your recruiter should provide you with information on what to expect before going in. 

We’ll dig deeper into the question types later in the article, but expect an emphasis on behavioral questions. Each interviewer is usually assigned two or three leadership principles to focus on during your interview. 

1.2 What exactly is Amazon looking for?

At the end of each interview your interviewer will grade your performance using a standardized feedback form that summarizes the attributes Amazon looks for in a candidate. That form is constantly evolving, but we have listed some of its main components below.

The interviewer will file the notes they took during the interview. This usually includes the questions they asked, a summary of your answers, and any additional impressions they had (e.g. communicated ABC well, weak knowledge of XYZ, etc).

B) Technical competencies

Your interviewer will then grade you on technical competencies . They will be trying to determine whether you are "raising the bar" or not for each competency they have tested. In other words, you'll need to convince them that you are at least as good as or better than the average current Amazon data scientist at the level you're applying for.

The exact technical competencies you'll be evaluated against vary by role. But here are some common ones for data science roles:

• Problem solving
• Data analysis and manipulation
• Machine learning / AI
• Business acumen

C) Leadership principles

Your interviewer will also grade you on Amazon's 16 leadership principles and assess whether you're "raising the bar" for those too. As mentioned above, each interviewer is given two or three leadership principles to grill you on. 

D) Overall recommendation

Finally, each interviewer will file an overall recommendation into the system. The different options are along the lines of: "Strong hire", "Hire", "No hire", "Strong no hire".

1.3 What happens behind the scenes

Your recruiter is leading the process and taking you from one stage to the next. Here's what happens after each of the stages we’ve just described:

• After the phone screens , your recruiter decides to move you to the onsite or not, depending on how well you've done up to that point.
• After the onsite , each interviewer files their notes into the internal system, grades you and makes a hiring recommendation. (i.e. "Strong hire", "Hire", "No hire", "Strong no hire")
• The "Debrief" brings all your interviewers together and is led by the Bar Raiser , who is usually the most experienced interviewer and is also not part of the hiring team. The Bar Raiser will try to guide the group towards a hiring decision. It's rare, but they can also veto hiring even if all other interviewers want to hire you.
• You get an offer. If everything goes well, the recruiter will then give you an offer, usually within a week of the onsite, but it can sometimes take longer.

It's also important to note that recruiters and people who refer you have little influence on the overall process. They can help you get an interview at the beginning, but that's about it.

2. Example questions

Let’s get into the four primary categories of questions you’ll answer during the Amazon data science interview:

• Coding (37% of reported questions)
• Machine Learning (27%)
• Behavioral (19%)
• Statistics (17%)

In the sections below, we've put together a high-level overview of each type of question. In addition, we've compiled a selection of real Amazon data scientist interview questions, according to data from Glassdoor . We've edited the language in some places to improve the clarity or grammar, and, when appropriate, we’ve included a link to a solution.

Many of these questions are asked in the form of case studies. For more information about data science case study interviews, take a look here .

Important Note: Amazon data scientists work across many divisions, such as Amazon Web Services, Alexa, SCOT, logistics, and more. As each role will have different responsibilities, the interview questions you receive will reflect that. The Glassdoor data we’ve used is generalized across all data scientist roles, so consult the preparation materials from your recruiter to know what areas will be the most important for your position.

2.1 Coding questions (37%)

Amazon data scientists must write code and develop sophisticated algorithms that synthesize data coming in from multiple sources. You’ll need to demonstrate that you have the technical knowledge necessary to analyze and manipulate that data.

Expect interviewers to test you on SQL, data structures, algorithms, and some modeling. Most candidates report solving data structure and algorithm questions using Python and solving modeling questions with Python or R.

In most cases you will be coding on a whiteboard (or the virtual equivalent), but some candidates have reported entirely verbal onsite interview rounds. This shows how important communication skills are to Amazon, so practice both writing your scripts on paper and speaking through your reasoning. 

Practice using the example questions below. For more help, use our list of 49 real Amazon coding interview questions .

Amazon data scientist interview questions: coding

• Write a SQL code to explain month to month user retention rate.
• Describe different JOINs in SQL.
• What is the most advanced query you’ve ever written?
• Given a table with three columns, (id, category, value) and each id has 3 or less categories (price, size, color); how can you find those id's for which the value of two or more categories matches one another? 
• I have table 1, with 1million records, with ID, AGE (column names) , Table 2 with 100 records with ID and Salary, and the following script. How many records would be returned? SELECT A.ID,A.AGE,B.SALARY FROM TABLE 1 A LEFT JOIN TABLE 2 B ON A.ID = B.ID + WHERE B.SALARY > 50000
• Given a csv file with ID and Quantity columns, 50million records, and the size of the data is 2gig, write a program to aggregate the QUANTITY column.

Data structure and algorithms

• Write a python code for recognizing if entries to a list have the same characters or not. Then what is the computational complexity of it?
• You have an array of integers and you want to find a certain element; what effective algorithm would you use and what is the efficiency of it?
• For a long sorted list and a short (4 element) sorted list, what algorithm would you use to search the long list for the 4 elements?
• Given an unfair coin with the probability of heads not equal to .5, what algorithm could you use to create a list of random 1s and 0s?
• Given a bar plot, imagine you are pouring water from the top. How do you qualify how much water can be kept in the bar chart? ( solution )
• Write a Python function that displays the first n Fibonacci numbers. ( solution )
• Suppose you have a list of strings, each of which is an English sentence. # Output a dictionary out_dict that maps a key n to the list of words that occur in n different sentences. # E.g. # Input: str_list = [ “The cat ate the fish”, “The cat saw the roses”, “The roses are red” ]
• If given an integer n and an array of numbers, give out the histogram divided into n bins.
• How would you improve a classification model that suffers from low precision?
• We have two models, one with 85% accuracy, one 82%. Which one do you pick? ( solution )
• When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?
• How do you inspect missing data and when are they important?
• Assume you have a file containing data in the form of data = [{"one":a1, "two":b1,...},{"one":a2, "two":b2,...},{"one":a3, "two":b3,...},...] How could you split this data into 30% test and 70% train data?

2.2 Machine learning questions (27%)

Amazon data scientists must develop services and solve problems that are endlessly complex and constantly evolving. So your interviewer will test your ability to build innovative algorithms that improve and remain accurate over time.

Depending on the role, your interviewer may ask you to define and discuss specific ideas around system design and machine learning models. More in-depth machine learning rounds will require you to build out a hypothetical model or discuss how to improve existing ones related to real-life Amazon business decisions.

According to Glassdoor, some general topics that have come up before on Amazon machine learning interviews include unsupervised machine learning, bias-variance tradeoff, PCA, and recurrent neural networks, in addition to the full questions below. 

Let’s get into them.

Amazon data scientist interview questions: machine learning

• How do you interpret logistic regression?
• How does dropout work?
• What is L1 vs L2 regularization?
• What is the difference between bagging and boosting?
• Explain in detail how a 1D CNN works.
• Describe a case where you have solved an ambiguous business problem using machine learning.
• Having a categorical variable with thousands of distinct values, how would you encode it?
• How do you manage an unbalanced data set?
• What is lstm? Why use lstm? How was lstm used in your experience?
• What did you use to remove multicollinearity? Explain what values of VIF you used.
• Explain different time series analysis models. What are some time series models other than Arima?
• How does a neural network with one layer and one input and output compare to a logistic regression?

2.3 Behavioral (19%)

Amazon’s leadership principles tie into every step of the interview process, and interviewers will test your affinity with them through behavioral questions. Even in the technical rounds, your interviewers are looking for you to live and breathe these 16 principles, so spend extra time studying them.

If you're not already familiar with Amazon's leadership principles, here is the full list:

• Customer Obsession - "Leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. Although leaders pay attention to competitors, they obsess over customers.”
• Ownership - "Leaders are owners. They think long term and don’t sacrifice long-term value for short-term results. They act on behalf of the entire company, beyond just their own team. They never say ‘that’s not my job.’”
• Invent and Simplify - "Leaders expect and require innovation and invention from their teams and always find ways to simplify. They are externally aware, look for new ideas from everywhere, and are not limited by ‘not invented here.’ Because we do new things, we accept that we may be misunderstood for long periods of time.”
• Are Right, A Lot - "Leaders are right a lot. They have strong judgement and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.”
• Learn and Be Curiou s - "Leaders are never done learning and always seek to improve themselves. They are curious about new possibilities and act to explore them.”
• Hire and Develop the Best - "Leaders raise the performance bar with every hire and promotion. They recognize exceptional talent, and willingly move them throughout the organization. Leaders develop leaders and take seriously their role in coaching others. We work on behalf of our people to invent mechanisms for development like Career Choice.”
• Insist on the Highest Standards - "Leaders have relentlessly high standards — many people may think these standards are unreasonably high. Leaders are continually raising the bar and drive their teams to deliver high quality products, services, and processes. Leaders ensure that defects do not get sent down the line and that problems are fixed so they stay fixed.”
• Think Big - "Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers.”
• Bias for Action - "Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk taking.”
• Frugality - "Accomplish more with less. Constraints breed resourcefulness, self-sufficiency, and invention. There are no extra points for growing headcount, budget size, or fixed expense.”
• Earn Trust - “Leaders listen attentively, speak candidly, and treat others respectfully. They are vocally self-critical, even when doing so is awkward or embarrassing. Leaders do not believe their or their team’s body odor smells of perfume. They benchmark themselves and their teams against the best.”
• Dive Deep - "Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. No task is beneath them.”
• Have Backbone; Disagree and Commit - "Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly.”
• Deliver Results - "Leaders focus on the key inputs for their business and deliver them with the right quality and in a timely fashion. Despite setbacks, they rise to the occasion and never settle.”
• Strive to be Earth’s Best Employer - “Leaders work every day to create a safer, more productive, higher performing, more diverse, and more just work environment. They lead with empathy, have fun at work, and make it easy for others to have fun. Leaders ask themselves: Are my fellow employees growing? Are they empowered? Are they ready for what's next? Leaders have a vision for and commitment to their employees' personal success, whether that be at Amazon or elsewhere.”
• Success and Scale Bring Broad Responsibility - “We started in a garage, but we're not there anymore. We are big, we impact the world, and we are far from perfect. We must be humble and thoughtful about even the secondary effects of our actions. Our local communities, planet, and future generations need us to be better every day. We must begin each day with a determination to make better, do better, and be better for our customers, our employees, our partners, and the world at large. And we must end every day knowing we can do even more tomorrow. Leaders create more than they consume and always leave things better than how they found them.”

As you prepare for your interviews, you'll want to be strategic about practicing "stories" from your past experiences that highlight how you've embodied each of the 16 principles listed above. We'll talk more about the strategy for doing this in section 3 below (the preparation section).

To help you start practicing, we've compiled the following list of behavioral questions asked at Amazon data science interviews. We recommend that you practice each of them.  In addition, we also recommend practicing the behavioral questions in our Amazon behavioral interview  guide , which covers a broader range of behavioral topics related to Amazon’s leadership principles.

In the questions below, we’ve suggested the leadership principle that each question may be addressing. For any principles that were not reflected in the Amazon data scientist interview questions on Glassdoor, we’ve added a question from the  Amazon SDE  interview.

Let's get to it.

Amazon data scientist interview questions: leadership principles (behavioral)

• Tell me about yourself.
• Tell me about a time you made something much simpler for customers. (Principle: Customer Obsession)
• Tell me about a project you worked on that was not successful. What would you do differently? (Principle: Ownership)
• What’s the most innovative idea you’ve ever had? (Principle: Invent and Simplify)
• Tell me about a time you applied judgement to a decision when data was not available. (Principle: Are Right, A Lot)
• Why data science? (Principle: Learn and Be Curious)
• Where do you see yourself within the next 5 years? (Principle: Hire and Develop the Best)
• How would you improve this [project on your resume] if you had more time? (Principle: Insist on the Highest Standards)
• Tell me a time that a goal was hard to achieve. What did you learn from that? (Principle: Insist on the Highest Standards)
• Tell me about your most significant accomplishment. Why was it significant? (Principle: Think Big)
• Did you come across a scenario where the deadline given to you for a project was earlier than expected? How did you deal with it and what was the result? (Principle: Bias for Action)
• Describe the last time you figured out a way to keep an approach simple or to save on expenses (Principle: Frugality)
• What is the one feedback/complaint you always get from your colleagues? How are you working on such feedback? (Principle: Earn Trust)
• Tell me a time you used the data to come up with data-driven statistics, and how did you present your findings? (Principle: Dive Deep)
• Describe the situation when you had a disagreement with your manager, and how did you handle that? (Principle: Have Backbone; Disagree and Commit)
• How would you measure the impact of a business initiative? (Principle: Deliver Results)
• Tell me about a time when you had two deadlines at the same time. How did you manage the situation? (Principle: Deliver Results)
• What is the composition of your current team, and how are you encouraging their growth? (Principle: Strive to be Earth’s Best Employer)
• How have you left a previous post better than you found it? (Principle: Success and Scale Bring Broad Responsibility)

Note: For the two questions related to the principles Strive to be Earth's Best Employer and Success and Scale Bring Broad Responsibility, we have created our own questions. As these principles are new at the time of publishing, we do not yet have Glassdoor data that addresses them.

2.4  Statistics questions (17%)

Amazon data scientists have to derive useful insights from large and complex datasets, which makes statistical analysis an important part of their daily work. Interviewers will look for you to demonstrate the robust statistical foundation needed in this role.

Review some fundamental statistics and how to give concise explanations of statistical terms, with an emphasis on applied statistics and statistical probability. Some general topics that interviewers have asked about in previous interviews include A/B testing, normalization, and Bayes theorem. 

In addition to these general topics, you’ll find complete questions to work through below.

Amazon data scientist interview questions: statistics

• What is p-value?
• What is the maximum likelihood of getting k heads when you tossed a coin n times? Write down the mathematics behind it.
• There are 4 red balls and 2 blue balls, what's the probability of them not being the same in the 2 picks?
• How would you explain hypothesis testing for a newbie?
• What is cross-validation?
• How do you interpret OLS regression results?
• Explain confidence intervals
• Name the five assumptions of linear regression
• Estimate the disease probability in one city given the probability is very low nationwide. Randomly asked 1000 people in this city, with all negative responses (NO disease). What is the probability of disease in this city?
• What is the difference between linear regression and a t-test?

3. How to prepare

Now that you know what questions to expect, let's focus on how to prepare. Below is our four-step prep plan for Amazon, which also applies to Amazon Web Services. If you're preparing for more companies than just Amazon, then check our generic data science interview preparation guide .

3.1 Learn about Amazon’s culture

Most candidates fail to do this. But before investing tens of hours preparing for an interview at Amazon, you should take some time to make sure it's actually the right company for you.

Amazon is prestigious, and it's tempting to assume that you should apply without considering things more carefully. But it's important to remember that the prestige of a job alone won't make you happy in your day-to-day work. It's the type of work and the people you work with that will.

If you know anyone who works at Amazon or used to work there, as a data scientist or in another role, talk to them to understand what the culture is like. The leadership principles we discussed above can give you a sense of what to expect, but there's no replacement for a conversation with an insider. 

Finally, we would also recommend reading the following resources:

• Amazon's technology culture video mix (by Amazon)
• Amazon vision and mission analysis (by Panmore Institute)
• Amazon strategy teardown (by CB Insights)

3.2 Practice by yourself

As mentioned above, you'll encounter four main types of interview questions at Amazon: coding, machine learning, statistics, and leadership principles. Use each category below to find resources to help you prepare.

To get an idea of larger innovations and research that you may be taking part in as an Amazon data scientist, take a look at Amazon.science . For more information about how to prepare for case studies, take a look at our guide to data science case interviews .

For the coding interview questions , start with the video below  that shows a step-by-step method by Amazon for answering programming questions. Practice the method using example questions such as those in section 2.1, or those relative to coding-heavy Amazon positions (e.g. Amazon software development engineer interview guide ). 

Also, practice SQL and programming questions with medium and hard level examples on LeetCode ,  HackerRank , or StrataScratch . Take a look at Amazon’s technical topics page , which, although it’s designed around software development, should give you an idea of what they’re looking out  for. For even more help with SQL, read this analysis of the 3 "types" of SQL problems. Note that in the onsite rounds you’ll likely have to code on a whiteboard without being able to execute it, so practice writing through problems on paper.

For machine learning and statistics questions , Brilliant.org offers online courses designed around statistical probability and other useful topics, some of which are free. Kaggle also offers free courses around introductory and intermediate machine learning, as well as data cleaning, data visualization, SQL, and others.

Search for specific questions and answers around statistics, machine learning, data analysis, and others on StackExchange . Finally, you can post your own questions and discuss topics likely to come up in your interview on Reddit’s statistics and machine learning threads.

For behavioral interview questions , we recommend learning our step-by-step method for answering behavioral questions.  You can then use that method to practice answering the example questions provided in section 2.3 above. This is especially important for Amazon’s leadership principles. Make sure you have at least one story or example for each of the principles, from a wide range of positions and projects.

Finally, a great way to practice all of these different types of questions is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it works.

3.3 Practice with peers

Practicing by yourself will only take you so far. One of the main challenges of data scientist interviews at Amazon is communicating your different answers in a way that's easy to understand.

As a result, we strongly recommend practicing with a peer interviewing you. If possible, a great place to start is to practice with friends. This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

3.4 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers

If you know a data scientist or someone who has experience running interviews at Amazon or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

General Topics

Amazon research scientist interview prep.

Any recommended study resources for Amazon RS roles? I see limited mock interview options as well. I want to give it my best shot, please share if you’re aware. Thanks so much! I have interviewed for a DS role at Amazon so I’m aware of their DS format. TC 140K

Can please provide details on how the DS interview at Amazon went?

Yes, of course. I had two phone screens which covered the following: Statistics and ML basics, programming in Python (LC Easy), SQL queries, project-related questions, leadership principles. The on-site covered all of the topics above, and also included a product round, a greater number of leadership principles / behavioral questions and more ML expertise.

Thank you for the response, can I dm you with more questions? as right now I have a Applied Scientist interview with AWS with the potential for a Data Scientist interview if I don't make it for the AS role

I mean this obviously depends on the group you are being hired into (and for what specialty). A RS hire for a group in last mile will most likely be tested on their knowledge of mixed integer linear programming and stochastic optimization vs say... a RS hired to do NLP for Alexa who might be tested on deep learning architecture and design. You might want to provide more information. In any case, LPs will always be tested so prepare for those properly.

It’s a healthcare role.

So sounds like LPs and domain knowledge?

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Plants can communicate and respond to touch. Does that mean they're intelligent?

Tonya Mosley

"The primary way plants communicate with each other is through a language, so to speak, of chemical gasses," journalist Zoë Schlanger says. Mohd Rasfan/AFP via Getty Images hide caption

"The primary way plants communicate with each other is through a language, so to speak, of chemical gasses," journalist Zoë Schlanger says.

In the 1960s and '70s, a series of questionable experiments claimed to prove that plants could behave like humans, that they had feelings, responded to music and could even take a polygraph test .

Though most of those claims have since been debunked, climate journalist Zoë Schlanger says a new wave of research suggests that plants are indeed "intelligent" in complex ways that challenge our understanding of agency and consciousness.

"Agency is this effect of having ... an active stake in the outcome of your life," Schlanger says. "And when I was looking at plants and speaking to botanists, it became very clear to me that plants have this."

In her new book, The Light Eaters: How the Unseen World of Plant Intelligence Offers a New Understanding of Life on Earth , Schlanger, a staff reporter at The Atlantic, writes about how plants use information from the environment, and from the past, to make "choices" for the future.

Happy Arbor Day! These 20 books will change the way you think about trees

Schlanger notes that some tomato plants, when being eaten by caterpillars, fill their leaves with a chemical that makes them so unappetizing that the caterpillars start eating each other instead. Corn plants have been known to sample the saliva of predator caterpillars — and then use that information to emit a chemical to attract a parasitic wasp that will attack the caterpillar.

Stop overwatering your houseplants, and other things plant experts want you to know

Schlanger acknowledges that our understanding of plants is still developing — as are the definitions of "intelligence" and "consciousness." "Science is there [for] observation and to experiment, but it can't answer questions about this ineffable, squishy concept of intelligence and consciousness," she says.

But, she adds, "part of me feels like it almost doesn't matter, because what we see plants doing — what we now understand they can do — simply brings them into this realm of alert, active processing beings, which is a huge step from how many of us were raised to view them, which is more like ornaments in our world or this decorative backdrop for our our lives."

Interview highlights

On the concept of plant "intelligence"

Intelligence is this thing that's loaded with so much human meaning. It's too muddled up sometimes with academic notions of intelligence. ... Is this even something we want to layer on to plants? And that's something that I hear a lot of plant scientists talk about. They recognize more than anyone that plants are not little humans. They don't want their subjects to be reduced in a way to human tropes or human standards of either of those things.

On the debate over if plants have nervous systems

I was able to go to a lab in Wisconsin where there [were] plants that had ... been engineered to glow, but only to glow when they've been touched. So I used tweezers to pinch a plant on its vein, ... the kind of mid-rib of a leaf. And I got to watch this glowing green signal emanate from the point where I pinch the plant out to the whole rest of the plant. Within two minutes, the whole plant had received a signal of my touch, of my "assault," so to speak, with these tweezers. And research like that is leading people within the plant sciences, but also people who work on neurobiology in people to question whether or not it's time to expand the notion of a nervous system.

On if plants feel pain

TED Radio Hour

Plants don't have brains — but they sure act smart.

We have nothing at the moment to suggest that plants feel pain, but do they sense being touched, or sense being eaten, and respond with a flurry of defensive chemicals that suggest that they really want to prevent whatever's going on from continuing? Absolutely. So this is where we get into tricky territory. Do we ascribe human concepts like pain ... to a plant, even though it has no brain? And we can't ask it if it feels pain. We have not found pain receptors in a plant. But then again, I mean, the devil's advocate view here is that we only found the mechanoreceptors for pain in humans, like, fairly recently. But we do know plants are receiving inputs all the time. They know when a caterpillar is chewing on them, and they will respond with aggressive defensiveness. They will do wild things to keep that caterpillar from destroying them further.

On how plants communicate with each other

Zoë Schlanger is a staff writer at The Atlantic. Heather Sten/Harper Collins hide caption

Zoë Schlanger is a staff writer at The Atlantic.

The primary way plants communicate with each other is through a language, so to speak, of chemical gasses. ... And there's little pores on plants that are microscopic. And under the microscope, they look like little fish lips. ... And they open to release these gasses. And those gasses contain information. So when a plant is being eaten or knocked over by an animal or hit by wind too hard, it will release an alarm call that other plants in the area can pick up on. And this alarm call can travel pretty long distances, and the plants that receive it will prime their immune systems and their defense systems to be ready for this invasion, for this group of chewing animals before they even arrive. So it's a way of saving themselves, and it makes evolutionary sense. If you're a plant, you don't want to be standing out in a field alone, so to speak. It's not good for reproductive fitness. It's not good for attracting pollinators. It's often in the interest of plants to warn their neighbors of attacks like this.

On plant "memory"

Research News

Orangutan in the wild applied medicinal plant to heal its own injury, biologists say.

There's one concept that I think is very beautiful, called the "memory of winter." And that's this thing where many plants, most of our fruit trees, for example, have to have the "memory," so to speak, of a certain number of days of cold in the winter in order to bloom in the spring. It's not enough that the warm weather comes. They have to get this profound cold period as well, which means to some extent they're counting. They're counting the elapsed days of cold and then the elapsed days of warmth to make sure they're also not necessarily emerging in a freak warm spell in February. This does sometimes happen, of course. We hear stories about farmers losing their crops to freak warm spells. But there is evidence to suggest there's parts of plants physiology that helps them record this information. But much like in people, we don't quite know the substrate of that memory. We can't quite locate where or how it's possibly being recorded.

On not anthropomorphizing plants

What's interesting is that scientists and botany journals will do somersaults to avoid using human language for plants. And I totally get why. But when you go meet them in their labs, they are willing to anthropomorphize the heck out of their study subjects. They'll say things like, "Oh, the plants hate when I do that." Or, "They really like this when I do this or they like this treatment." I once heard a scientist talk about, "We're going to go torture the plant again." So they're perfectly willing to do that in private. And the reason for that is not because they're holding some secret about how plants are actually just little humans. It's that they've already resolved that complexity in their mind. They trust themselves to not be reducing their subjects to human, simplistic human tropes. And that's going to be a task for all of us to somehow come to that place.

It's a real challenge for me. So much of what I was learning while doing research for this book was super intangible. You can't see a plant communicating, you can't watch a plant priming its immune system or manipulating an insect. A lot of these things are happening in invisible ways. ... Now when I go into a park, I feel totally surrounded by little aliens. I know that there is immense plant drama happening all over the place around me.

Sam Briger and Susan Nyakundi produced and edited this interview for broadcast. Bridget Bentz and Molly Seavy-Nesper adapted it for the web.

Computer Science Professors Earn Amazon Research Awards

Professors Niranjan Balasubramanian and Michalis Polychronakis, from Stony Brook University’s Department of Computer Science , have each received Amazon Research Awards to further advance their fields of research.

Balasubramanian ‘s research focuses on the potential of large language models (LLMs) for autonomous execution of complex tasks. He will use the Amazon funding to create a controlled environment, a complex task testbed, where LLMs can be rigorously evaluated. This testbed features innovative assessment criteria beyond typical accuracy metrics, a sandbox execution environment with mock APIs, and natural language descriptions of complex goals.

This research bridges the gap between theoretical promise and real-world implementation. By developing a controlled environment for LLMs, Balasubramanian aims to unlock their potential while ensuring safety. His work contributes to advancing AI technologies and addressing real-world challenges in a thoughtful and systematic manner.

Polychronakis ’ Amazon funding will allow him to continue to explore ways to improve software security and enhance memory safety. His research aims to address the challenges posed by memory corruption vulnerabilities, which are still a major source of system compromise and malware infection. Despite the advantages of modern memory-safe languages like Go and Rust, most existing software is still written in memory-unsafe languages like C and C++. The familiarity of developers with C and C++, vast code bases in these languages, and their efficiency hinder efforts to migrate to memory-safe alternatives.

To address this issue, Polychronakis is developing SafeTrans, a system that automates the conversion of existing C/C++ code to Rust. Rust, with its memory safety features and low runtime overhead, is a great candidate to replace memory-unsafe languages in critical systems. SafeTrans seeks to accelerate the adoption of memory-safe languages by automating elements of the migration process while lowering the risk of memory-related vulnerabilities. His research advances the larger goal of increasing software security and making systems more resistant to modern vulnerabilities.

Both researchers received approximately $100K in funding, which includes Amazon credits.

The Amazon Research awards recognize the innovative contributions of Balasubramanian and Polychronakis, and their research assistants. Both initiatives demonstrate a commitment to innovation and real-world impact, shaping the future of technology.

— Sahil Sarna

This story originally appeared on the Computer Science website .

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Brewer interviewed for press herald report on 2024 presidential election.

University of Maine chair and professor of political science Mark Brewer was interviewed by the Portland Press Herald for his input on voter turnout for the 2024 presidential election. Brewer said turnout is always higher in presidential years as opposed to nonpresidential years. “Right now, a fairly large percentage of voters are expressing dislike of their presidential choices and/or lack of interest in the race, but this will change as we get closer to Election Day and voters realize the stakes in this election,” said Brewer.

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Amazon Interview Experience for SDE 2 (L5) Hyderabad April 2024 [got the Offer letter]

Introduction, in february to april 2024, i had the chance to interview with amazon for the sde 2 (l5) role. with 2.8 years of previous experience as an sde 3 at a p-based recruiters mnc, i was eager to explore this new opportunity. here’s a detailed account of my interview journey., application and initial contact.

One of the Amazon recruiters reached out to me on LinkedIn and requested me to share my resume over email. Within a week, received the online assessment link followed by 4 round of interview over the P-based next 2 months.

Company: Amazon

Position: SDE 2 (L5)

Interview Dates: Feb – March 2024

Offer Date: 2nd week of April 2024

Online Assessment

Question 1:

• Return the longest decreasing subsequence in a linked list.
• Similar to the Longest Increasing Subsequence

Question 2:

• Given two categories of movies with start time and duration.
• Find the minimum time by which you can finish watching at least 1 movie of each category.

Round 1: Low-Level a Design (1 hr.)

• Introduction and Leadership Principles: We started with introductions, followed by a couple of leadership questions like give a situation where you had to deep dive in code.
• Design Question: Design a Chat Messenger Like WhatsApp or Facebook Messenger

Round 2: Coding (1 hr.)

• Introduction and Leadership Principles: We started with introductions, followed by a couple of leadership questions like give a situation where you aimed for long term goal rather than short term.

Given a matrix containing blue and white balloons, where on any given day, all the white balloons surrounding the blue ones in four directions (up, down, left, right) are converted to blue ones. Return the minimum number of days required to convert all balloons to blue.

How to get the number of blue balloons at any given day (0 to MAX_DAY)

Explanation:

Approach: BFS

• Amazon Provides a locker service for packages
• Design a function which returns the locker to be open when a package is received
• Design a function to retrieve the package of a given customer

For simplicity, assume packages can be of 3 sizes: small, medium and large.

Verbally discussed the algorithm and data structures due to lack of time.

Round 3: Hiring Manager, High Level Design (1 hr.)

Introduction and Leadership Principles: We started with introductions, followed by a couple of leadership questions and discussion on my project.

• Question: Design Auto Complete / search recommendations in Amazon Search
• Assumption: Given a ranking algorithm which returns the search priority.

Target: Show top 5 searches.

Round 4: Bar Raiser, Coding (1 hr.)

Introduction and Leadership Principles: We started with introductions, followed by a couple of leadership questions.

• Question 1: Given a Binary Tree, return the maximum sum of the subset such that no two nodes in any subset are adjacent.
• Question 2: Print the subset for last question

I completely solved question 1 with a dry run and discussed verbally how to improve the algorithm for printing the subset. I received the hiring feedback within a week, and the compensation details were finalized the following week.

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Being Green

A new book marvels at the strangeness of plants—and tries a little too hard to explain how they’re like people..

Back when I only visited the countryside occasionally, I suffered from what botanists call “plant blindness,” described by Zoë Schlanger in her entertaining new book, The Light Eaters , as “the tendency to view plant life as an indistinguishable mass, a green smudge, rather than as thousands of genetically separate and fragile individuals, as distinct from one another as a lion is from a trout.” Now that I live in small-town New England, I’m a bit more literate in the flora I see. A stand of Japanese knotweed , the bamboo-like invasive I’m constantly beating back to the margins of my own yard, indicates there must be a brook or other waterway out there, since that’s how the plant spreads. One wild apple tree by the roadside may have sprouted from a core tossed out of a car, but two or more suggests an old farmstead now consumed by the surrounding brush. I now know I will pine in vain forever for my own patch of partridge berry , a ground cover that flourishes along my favorite trail because it obviously prefers growing in pine needles instead of under the deciduous trees in my yard. But above all, I know that a lifetime of study couldn’t tell me everything that’s going on out there in the green. And the thrill I get every time a seed I’ve planted germinates never dims.

The vegetable kingdom is full of wonders and mysteries, as Schlanger lavishly demonstrates in The Light Eaters. For one, plants created Earth’s atmosphere, oxygenating it and making it breathable for animals like us. Likewise, she explains, “every thought that has ever passed through your brain was made possible by plants,” because every animal organ is made out of sugars produced by photosynthesis, the wizardry through which plants transform light and air into the fuel that built and powers our bodies.

Yet are we grateful? We take plants for granted and seldom spend much of the brainpower they supply on considering their multiplicity and unsung abilities. When it comes to plant blindness, though, Schlanger may be overstating her case, given that somewhere between 55 and 80 percent of Americans participate in some form of gardening. (Admittedly, that’s according to surveys conducted by various players in the gardening industry, but doesn’t everyone know at least one fanatical gardener? I have a friend who accuses me of paying more attention to the fruit tree saplings I’ve planted than to my actual family.) Nevertheless, Schlanger is probably right in thinking that most modern humans regard plants as alive but not animate —a bit boring by the standards of creatures that can move around freely.

The Light Eaters

By Zoë Schlanger. Harper.

Slate receives a commission when you purchase items using the links on this page. Thank you for your support.

What few of us know about, and what Schlanger makes the focus of The Light Eaters, is the controversy raging among botanists about plant behavior—and even about using the word “behavior” with regard to plants—resulting in career-scuttling opprobrium and professional taboos. Forget the Necronomicon , that dread tome from the fiction of H.P. Lovecraft. If ever there was a cursed book it’s Peter Tompkins and Christopher Bird’s 1973 bestseller, The Secret Life of Plants, which convinced people that their houseplants enjoyed being talked to and preferred listening to classical music over rock. While Schlanger describes the book as “a beautiful collection of myths,” the irresponsible and unsubstantiated claims it made tainted the entire field of plant behavior research. Botanists told Schlanger that in the years following The Secret Life of Plants , “the National Science Foundation became more reluctant to give grants to anyone studying plants’ responses to their environment.”

Only in the recent past has this cloud passed from the face of botany, allowing researchers to celebrate such wonders as a Chilean vine that conceals itself from herbivores by mimicking the leaves of nearby plants of entirely different species. Plant-friendly readers have probably already learned (from such popular books as Peter Wohlleben’s The Hidden Life of Trees ) that roots have symbiotic relationships with fungi in the soil, allowing them to connect to other roots to transmit information to and from their neighbors. Plants, as the scientists Schlanger interviews have learned, can also respond to assaults from, say, leaf-munching caterpillars by changing their own chemical composition to repel or even poison the attackers. They can then pump scents into the air to warn other plants to armor up as well or to attract predators who feed on the pests. Some of them can even tell whether a nearby plant of the same species is a close genetic relative and moderate their competition for nutrients accordingly. “The more botanists uncovered the complexity of forms and behaviors of plants,” Schlanger writes of these changing views, “the less the traditional assumptions about plant life seemed to apply.”

The Light Eaters is one of those science books in which the author travels to richly described locales to interview assorted researchers at work and to vividly describe their discoveries for a general audience. (Ed Yong’s An Immense World, about the sensory universe of animals, is the best-known recent example.) In addition to identifying that Chilean vine firsthand, Schlanger hikes through Pacific Northwest rainforests, strolls through gingko-tree groves in Virginia, visits sagebrush fields in California, and drops in on the 1800s Scottish farmhouse where an 83-year-old pioneer in the study of plant behavior grumpily acknowledges his belief “that plants are probably intelligent, and that intelligence is probably a property of all living things.”

This assertion is something of a holy grail for Schlanger, who spends much of the book seeking confirmation from her scientist subjects that plants could be “intelligent” and perhaps even possess “consciousness.” The fact that there isn’t a scientific consensus on how to define either of those terms makes it especially difficult to pin them to an edge case like plants, which don’t have brains or nervous systems. Plants do exhibit behavior , of a sort—they react to their environments, and some even seem to retain “memories” of, say, the time of day when pollinators visit. But without a clear understanding of what it means to be intelligent or conscious, it’s hard to say if this qualifies as either. For herself, Schlanger decides that intelligence means “the ability to learn from one’s surroundings and make decisions that best support one’s life,” and that plants meet this criterion.

This quest, while it provides a narrative thread for the book, becomes the one thorn among the otherwise lush pleasures of The Light Eaters. Why is it so important to Schlanger that plants be acknowledged as “intelligent” by these humans? In addition, she often seeks out evidence that plants exhibit a set of qualities prized by the woozy, vaguely liberal sentimentalism that’s common in popular nature books. (I’m looking at you, The Hidden Life of Trees .) Plants aren’t just individual monads competing in a ruthless Darwinian struggle to survive, she observes—they cooperate and help each other. Plants challenge the line drawn between the “spiritual and scientific worlds.” Plant sexuality “gleefully defies heteronormative modes of reproduction.” Plants demonstrate that nurture matters just as much as nature, debunking the “all-or-nothing thinking to which Western science tends to be devoted.”

The reasons for this very common way of framing nature writing are obvious. Human beings (a group to which—whatever the formidable abilities of plants—every potential reader belongs) can’t resist drawing parallels between themselves and other creatures. We are forever searching for “lessons” about how to conduct ourselves from the behavior of living things with whom we often don’t have much in common. There’s a long history of justifying various human actions by referring to dubious beliefs about how “nature” works. Proving that plants cooperate with each other becomes an argument for why people should as well. Schlanger writes that we ought to pay attention to the “social, collective intelligence” of plants because “we may be missing a big part of the story of our own existence without it.”

Nevertheless, in the final chapter of The Light Eaters, Schlanger suddenly, briefly rolls all this back. Rejecting the anthropomorphism that permeates the preceding 10 chapters, she cautions that “putting too human a sheen on plant intelligence is a failure of imagination.” Indeed. As enjoyable as The Light Eaters is, this failure does limit it. Where Yong made An Immense World more wondrous by emphasizing the dissimilarities between human and animal perception, until that final chapter Schlanger seems to insist that if we can’t identify with plants and see forms of our own experience in their very alien way of being, we will continue to disrespect and ignore them.

But one of the things we love most in plants is the enormous difference between them and us. The human looking to decompress from a rough day at the office certainly doesn’t take a walk in the woods because trees are like people. The strangeness of plants; their (apparent) stillness and slowness; their resilience; their ability to survive on air, water, and dirt; their capacity to transform garbage into food and desolation into beauty, all in the course of pursuing their own unfathomable business: These are the unsung miracles that surround us daily. At its best, The Light Eaters ushers those marvels onto center stage. These characteristics don’t need to be the result of “intelligence”—whatever that is—or any other trait or behavior or idea we’d like to see more of in our human compatriots. They may well be the product of forces so profoundly other that we’ll never entirely understand them, although it’s exciting to try. A window is always better than a mirror.

February 12, 2024

Pseudoscience Has Long Been Used to Oppress Transgender People

Three major waves of opposition to transgender health care in the past century have cited faulty science to justify hostility

By G. Samantha Rosenthal & The Conversation US

An activist holds a poster at a protest supporting the transgender community in Canada.

Artur Widak/NurPhoto via Getty Images

The following essay is reprinted with permission from The Conversation , an online publication covering the latest research.

In the past century, there have been three waves of opposition to transgender health care.

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In 1933, when the Nazis rose to power, they cracked down on transgender medical research and clinical practice in Europe. In 1979, a research report critical of transgender medicine led to the closure of the most well-respected clinics in the United States. And since 2021, when Arkansas became the first U.S. state among now at least 21 other states banning gender-affirming care for minors, we have been living in a third wave.

In my work as a scholar of transgender history , I study the long history of gender-affirming care in the U.S., which has been practiced since at least the 1940s. Puberty blockers , hormone therapies and anatomical surgeries are neither experimental nor untested and have been safely administered to cisgender, transgender and intersex adults and children for decades.

On the other hand, the archives of transgender medicine demonstrate that backlash against these practices has historically been rooted in pseudoscience. And today, an anti-science movement that aims to discredit science altogether is fueling the fire of the current wave of anti-trans panic.

The 1930s − eugenics and sexology collide

In the 1920s, the new science of hormones was just reaching maturation and entering mainstream consciousness . In the field of sexology – the study of human sexuality, founded in 19th century Europe – scientists were excited about research on animals demonstrating that removing or transplanting gonads could effectively change an organism’s sex.

In 1919, the German sexologist Magnus Hirschfeld founded the Institut für Sexualwissenschaft in Berlin, which became the world’s leading center for queer and transgender research and clinical practice. Hirschfeld worked closely with trans women as co-researchers throughout the 1920s. Several trans women also received care at the institute, including orchiectomies that halted the production of testosterone in their bodies.

Within months of Hitler’s rise to power in early 1933, a mob of far-right students broke into and shuttered the institute for being “ un-German .” Some of the most famous images of Nazi book burning show the institute’s library set ablaze in an outdoor plaza.

Nazi ideology was based on another prominent field of science of that time: eugenics , the belief that certain superior populations should survive while inferior populations must be exterminated. In fact, Hirschfeld’s sexology and Nazi race science had common roots in the Enlightenment-era effort to classify and categorize the world’s life forms.

But in the late 19th century, many scientists went a step further and developed a hierarchy of human types based on race, gender and sexuality. They were inspired by social Darwinism , a set of pseudoscientific beliefs applying the theory of survival of the fittest to human differences. As race scientists imagined a fixed number of human races of varying intelligence, sexologists simultaneously sought to classify sexual behaviors as innate, inherited states of being: the “homosexual” in the 1860s and the “transvestite,” a term coined by Hirschfeld himself, in 1910.

But where Hirschfeld and other sexologists saw the classification of queer and trans people as justifications for legal emancipation, eugenicists of the early 20th century in the U.S. and Europe believed sexually transgressive people should be sterilized and ultimately eradicated.

Based on this premise, the Nazis murdered thousands of LGBTQ people in the Holocaust.

The 1970s − making model citizens

In the 1950s and 1960s, transgender medicine bounced back in the U.S. Scientists and clinicians at several universities began experimenting with new hormonal and surgical interventions . In 1966, Johns Hopkins became the first university hospital in the world to offer trans health care.

By the 1970s, trans medicine went mainstream. Nearly two dozen university hospitals were operating gender identity clinics and treating thousands of transgender Americans. Several trans women and men wrote popular autobiographical accounts of their transitions. Trans people were even on television , talking about their bodies and fighting for their rights.

Yet trouble was brewing behind the scenes. Jon Meyer, a psychiatrist at Johns Hopkins, was skeptical of whether medical interventions really helped transgender people. In 1979, Meyer, along with his secretary Donna Reter, published a short academic paper that ushered in the second wave of historic backlash to trans medicine.

In their study, Meyer and Reter contacted previous patients of the Johns Hopkins Gender Identity Clinic. To understand whether surgery had improved patients’ lives, the authors developed an “adjustment scoring system.” They assigned points to patients who were in heterosexual marriages and had achieved economic security since their operations, while deducting points from those who continued to engage in gender nonconformity, homosexuality, criminality, or sought mental health care.

Meyer and Reter believed that gender-affirming surgeries were successful only if they made model citizens out of transgender people: straight, married and law-abiding.

In their results, the authors found no negative effects from surgery, and no patients expressed regret. They concluded that “sex reassignment surgery confers no objective advantage in terms of social rehabilitation,” but it is “subjectively satisfying” to the patients themselves. This was not a damning conclusion.

Yet, within two months, Johns Hopkins had shuttered its clinic . The New York Times reported that universities would feel pressure to similarly “curtail their operations and discourage others from starting to do them.” Indeed, only a handful of clinics remained by the 1990s. Transgender medicine did not return to Johns Hopkins until 2017 .

In requiring trans patients to enter straight marriages and hold gender-appropriate jobs to be considered successful, Meyer and Reter’s study was homophobic and classist in design . The study exemplified the pseudoscientific beliefs at the heart of transgender medicine in the 1960s through the 1980s, that patients had to conform to societal norms – including heterosexuality, gender conformity, domesticity and marriage – in order to receive care. This was not an ideology rooted in science but in bigotry.

The 2020s − distrust in science

As in the 1930s, opposition to trans medicine today is part of a broad reactionary movement against what some far-right groups consider the “ toxic normalization ” of LGBTQ people.

Legislators have removed books with LGBTQ content from libraries and disparaged them as “filth .” A recent law in Florida threatens trans people with arrest for using public restrooms. Both Florida and Texas have pursued efforts to compile data on their trans citizens . Donald Trump’s campaign platform calls for a nationwide ban on trans health care for minors and severe restrictions for adults.

And similar to the 1970s, opponents of trans medicine today frame gender-affirming care as a “debate,” even though all major U.S. medical associations support these practices as medically necessary and lifesaving.

But widespread distrust in science and medicine in the wake of the COVID-19 pandemic has affected how Americans perceive trans health care. Prohibitions on gender-affirming care have occurred simultaneously with the relaxing of pandemic restrictions, and some scholars argue that the movement against trans health care is part of a broader movement aimed at discrediting scientific consensus.

Yet the adage “ believe in science ” is not an effective rejoinder to these anti-trans policies. Instead, many trans activists today call for diminishing the role of medical authority altogether in gatekeeping access to trans health care . Medical gatekeeping occurs through stringent guidelines that govern access to trans health care, including mandated psychiatric evaluations and extended waiting periods that limit and control patient choice.

Trans activists have fought with the World Professional Association for Transgender Health , the organization that maintains these standards of care, by demanding greater bodily autonomy and depathologizing transsexuality. This includes pivoting to an informed consent model where patients make decisions about their own bodies after discussing the pros and cons with their doctors. Trans activists have been rallying against medical authority since the early 1970s, including calling for access to hormones and surgeries on demand .

It is not clear how the current third wave of backlash to transgender medicine will end. For now, trans health care remains a question dominated by medical experts on one hand and people who question science on the other.

This article was originally published on The Conversation . Read the original article .

Cyberattack on major health-tech company was caused by weak security infrastructure, Northeastern cybersecurity experts say

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A nearly weeklong cyberattack at Change Healthcare has caused prescription delays at thousands of pharmacies throughout the country, highlighting the fragility of our health care systems and their reliance on third-party software makers for key infrastructure, says Kevin Fu, a Northeastern college of engineering professor and cybersecurity expert.   

“I think it’s really a house of cards,” says Fu. “I think a lot of times companies, whether they are big or small, don’t realize how much they depend upon thousands of pieces of software. This particular [software] happens to be keystone to the whole practice of the delivery of health care. It’s deeply embedded into pharmacies. That’s why we are seeing these outages.” 

Change Healthcare is a health-tech company that provides thousands of pharmacies and health care providers in the U.S. with tools that allow them to process claims and other essential payment and revenue management practices. The company reported it was under a cyberattack last Wednesday. 

A day later, it informed the U.S. Securities and Exchange Commission of the incident, noting that it had “identified a suspected nation-state associated cyber security threat actor who had gained access to some of the Change Healthcare information technology systems.” 

In response to the attack, the company, which is a subsidiary of United Healthcare, took its systems offline as it worked to investigate and resolve the issue, causing prescription delays at pharmacies like CVS and Walgreens.  

As of Tuesday, Feb. 27, its systems remain offline , but 90% of the pharmacies affected by the attack have found workarounds to continue to provide services to customers, according to a statement Change Healthcare’s parent company, UnitedHealth, provided to CNBC.

Reuters has reported the attack was carried out by hackers who are part of the notorious ransomware gang Blackcat. Change Healthcare representatives, however, have not confirmed that or shared more details on the attackers. 

Fu says the fact that the company had to shut down its systems at all is a major indication that its systems were not designed properly with cybersecurity in mind. 

“If the cybersecurity designs were done right, we wouldn’t have needed to pull the plug, but there’s quite a lot of legacy software out there that is simply not resilient against an adversary,” he says. “Essential clinical functions need to be available for performing, whether or not the network goes down. … But today, the way things are written it’s all too common that if one piece goes down, the entire house of cards falls as well.” 

Aanjhan Ranganathan , a professor in the Khoury College of Computers Sciences and cybersecurity expert, says these attacks highlight the need for systems that are more distributed, less tied down, and more flexible and resilient in the face of attack. 

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“I think the biggest lesson again and again that these attacks are teaching us is the requirement for decentralized systems, being able to not have a single point of failure.” 

Building these kinds of systems is not easy, Ranganathan explains, as it often requires operators to rethink and rebuild their networking systems from the ground up. 

“It’s one of those things where you always go for functionality and you don’t build systems with security and privacy by design,” he says. “There has been a recent trend with building systems with privacy and security by design.” 

But what does a decentralized cybersecurity system look like? 

“For example, you could first of all, not store everything in one place,” says Ranganathan. “You could store all critical data in multiple places with different keys. There are ways in which you can store parts of the data in different places, and even if one part is inaccessible, you can recover that part based on information that you have in other places. By doing this you are forcing an attacker to successfully target more than one endpoint.” 

He adds, “You’re kind of building the infrastructure in such a way that there is no one place to take down the entire system. You have to take down many different parts of the puzzle to actually cause any impact.” 

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Senior Principal Research Scientist , Sustainability

Job ID: 2541506 | Amazon.com Services LLC

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Join us at the cutting edge of Amazon's sustainability initiatives to work on environmental and social advancements to support Amazon's long term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. The Worldwide Sustainability (WWS) organization capitalizes on Amazon’s scale & speed to build a more resilient and sustainable company. We manage our social and environmental impacts globally, driving solutions that enable our customers, businesses, and the world around us to become more sustainable. Sustainability Science and Innovation (SSI) is a multi-disciplinary team within the WW Sustainability organization that combines science, analytics, economics, statistics, machine learning, product development, and engineering expertise. We use this expertise and skills to identify, develop and evaluate the science and innovations necessary for Amazon, customers and partners to meet their long-term sustainability goals and commitments. We’re seeking a Senior Principal Scientist for Sustainability and Climate to drive technical strategy and innovation for our long-term sustainability and climate commitments through the latest science trends and emergent technologies, including AI & ML. You will serve as the strategic technical advisor to science, emerging tech, and climate pledge partners operating at the Director, VPs, and SVP level. You will set the next generation modeling standards for the team and tackle the most immature/complex modeling problems following the latest sustainability/climate sciences. You will stay current with emergent sustainability/climate science and machine learning trends, translate for leadership and be the external voice of our interpretation. You will nurture a continuous delivery culture to embed informed, science-based decision-making into existing mechanisms, such as decarbonization strategies, ESG compliance, and risk management. You will collaborate with the team to define emergent strategy for The Climate Pledge (TCP) based on emergent science and tech trends and influence investment strategy. You’ll also participate in worldwide sustainability organizational planning, hiring, mentorship and leadership development. If you see yourself as a thought leader and innovator at the intersection of climate science and tech, we’d like to meet you. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA

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