Amazon Data Scientist Interview (Process, Questions, Prep)

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".

2.3 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.

3. Amazon Data Scientist Interview Example Questions ↑

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

1. Coding/DSA/Modeling (35% of reported questions)
2. Machine Learning (25%)
3. Behavioral (20%)
4. Statistics (19%)\

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 (AWS), Alexa, Supply Chain Optimization Technologies (SCOT), logistics, and more. As each role will have different responsibilities, the interview questions you receive will reflect these. 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.

 3.1 Coding/DSA/Modeling questions (35%) ↑

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, some modeling, and data structures and algorithms (DSA). Most candidates report solving modeling questions with Python or R and solving data structure and algorithm questions using Python.

Vivek, a former Amazon data scientist, adds, "There are two rounds for SQL and coding. The best way to prepare for these are Leetcode practice problems. The level of difficulty is medium. So solving Leetcode medium problems for SQL and coding are adequate."

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.

3.2 Machine learning questions (25%) ↑

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, principal component analysis (PCA), XGBoost, Random Forest, LightGBM, and recurrent neural networks, in addition to the full questions below.

Let’s get into them.

3.3 Behavioral questions (20%) ↑

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:

1. 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.”
2. 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.’”
3. 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.”
4. Are Right, A Lot - "Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.”
5. Learn and Be Curious - "Leaders are never done learning and always seek to improve themselves. They are curious about new possibilities and act to explore them.”
6. 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.”
7. 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.”
8. 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.”
9. Bias for Action - "Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk taking.”
10. 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.”
11. 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.”
12. 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.”
13. 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.”
14. 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.”
15. 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.”
16. 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 4 below).

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 guide.

Let's get to it.

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 fairly new at the time of publishing, we do not yet have Glassdoor data that address them.

3.  Statistics questions (19%) 

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.

4. Amazon Data Scientist Interviewing Tips ↑

You might be a fantastic data scientist, but unfortunately, that won’t necessarily be enough to ace your interviews at Amazon. Interviewing is a skill in itself that you need to learn.

Let’s look at some key tips to make sure you approach your interviews in the right way. 

4.1 Ask clarifying questions

Often the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem. Most of the questions will focus on testing your technical proficiency.

4.2 Be conversational

Amazon wants to know if you have excellent communication skills. So make sure you approach the interview like it's a conversation.

Since Amazon will also be testing you on your ability to communicate highly technical concepts to non-technical people, be sure to brush up on your basics and practice interpreting them in a way that’s clear and easy for everyone to understand.

4.3 Think out loud

You need to walk your interviewer through your thought process before you actually start coding. Amazon recommends that you talk even while coding, as they want to know how you think. Your interviewer may also give you hints about whether you’re on the right track or not.

4.4 State and check assumptions

You need to explicitly state assumptions, explain why you’re making them, and check with your interviewer to see if those assumptions are reasonable.

4.5 Present multiple possible solutions

Present multiple possible solutions if you can. Amazon wants to know your reasoning for choosing a certain solution. Amazon also wants to see how well you collaborate. So when solving problems, don’t hesitate to ask further questions and discuss your solutions with your interviewers.

Also, if you have a moonshot idea, go for it. Amazon likes candidates who think freely and dream big. So if the question allows it, try to find a way to display your creative and innovative thinking. 

4.6 Be honest and authentic

Be genuine in your responses. Amazon interviewers appreciate authenticity and honesty. If you faced challenges or setbacks, discuss how you improved and learned from them. If you’re asked about your failures, don’t disguise them as strengths. Amazon values intellectual humility; admit where you went wrong and what you were able to learn from the failure.

4.7 Align your responses with Amazon’s Leadership Principles

This deserves mentioning here again, as Amazon interviews emphasize alignment with its leadership principles. Familiarize yourself with Amazon Leadership Principles and align your responses with them, especially behavioral responses.

4.8 Brute force, then iterate

When coding, don’t necessarily go for the perfect solution right away. First try and find a solution that works, then iterate to refine your answer.

4.9 Keep your code organized

Make sure to keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Amazon wants to see that your code has captured the right logical structure.

4.10 Get comfortable with coding on various medium

Amazon now typically asks interviewees to code in an online document file. But this can vary; it could be on a physical whiteboard or a virtual one. Check with your recruiter what it will be and practice it a lot.

5. Preparation Plan↑

Now that you know what questions to expect, let's focus on how to prepare. After all, the right preparation will make the difference between failing your Amazon interviews and getting an offer.

Below is our four-step prep plan for Amazon data scientist candidates. If you're preparing for more companies than just Amazon, then check our general data science interview preparation guide.

5.1 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 therefore tempting to assume that you should apply without considering things more carefully. However, it's important to remember that prestige alone won't make you happy in your day-to-day work. What will make you happy is what you’ll actually be doing as well as the people you'll be working with.

If you know data scientists who work at Amazon or used to work there, talk to them to understand what the culture is like. In addition, we would 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)

5.2 Practice by yourself

As mentioned above, you'll encounter four main types of interview questions at Amazon: coding/modeling/DSA, machine learning, statistics, and behavioral (aligned with Amazon Leadersip Principles) questions. 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 3.3 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.

5.3 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.

However, be warned, as you may come up against the following problems

• It’s hard to know if the feedback you get is accurate.
• They’re unlikely to have insider knowledge of interviews at your target company.
• On peer platforms, people often waste your time by not showing up.

For these reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert.

5.4 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.

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.

Find an Amazon data scientist interview coach so you can:

• Test yourself under real interview conditions
• Get accurate feedback from a real expert
• Build your confidence
• Get company-specific insights
• Learn how to tell the right stories, better
• Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!

Click here to book Amazon data scientist mock interviews with experienced interviewers.