Crack the Amazon Data Scientist Interviews | Ex-FAANG Data Scientist
By: Dan Lee
Tags: Data Scientist, Interview Questions, Data Science, Machine Learning, Python

Do you aspire to become a Data Scientist, ML Engineer, Applied Scientist or Research Scientist at Amazon?

This guide will provide you comprehensive details about the interview process and preparation tips to help you ace the data interviews at Amazon.

Hi, I’m Dan, the founder of datainterview.com and a former data scientist at PayPal and Google.

Here’s me in my Noogler mode when I joined Google as a data scientist in 2019 :)

I created dataInterview.com to help a candidate such as yourself ace data science interviews and land your dream role at a top company. Make sure to check it out!

Before we start, please note that that the exact interview experience at Amazon can vary given the role, team, and interviewer’s preference. In general, the details and tips provided should be helpful with your interview prep.

Table of Contents

About Amazon

As you might already know, Amazon is a conglomerate of multiple businesses from e-commerce (Amazon.com), consumer tech (Alexa), cloud computing (AWS), streaming (Prime + Twitch), and e.t.c. Given this horizontal scale, Amazon offers a lot of job opportunities in data-roles across various teams such as:

Search — Develop and employ a search ranking and recommender system on products offered on Amazon.com. AWS — Function as an external-facing consultant supporting AWS customers such as enterprise clients, or improve the engineering and user experience of the AWS interface. Alexa — Apply information retrieval, search and Q&A systems to support Amazon’s core consumer-based AI product, Alexa. Supply Chain Team (SCOT) — Apply quantitative analysis and modeling to automate and optimize Amazon’s supply chain of physical goods. Amazon GO — Improve the shopper’s experience on Amazon’s GO stores. Prime Video — Improve customer onboarding and engagement of Amazon’s streaming service. Fashion Technology — Apply computer vision and machine learning to provide personalized customer experiences for shoppers in the fashion category. Finance — A cross-functional team that supports various finance teams across Amazon’s businesses. Primarily, the team focuses on financial modeling and analytics. Security — Build fraud and spam detection systems to detect and prevent maladaptive users.

The bottomline is that Amazon provides various roles. So, ultimately, knowing the team interviewing you is an important attribute when preparing for interviews.

1. Roles

Let’s take a look at data roles typically hired at Amazon. Note in advance that, despite the distinctions in each role, the data fundamentals required are the same. The candidate must possess strong foundations in statistics, probability, machine learning, and coding. In addition, understanding Amazon’s leadership principles is vital.

Data Scientist

The core focus of data scientists at Amazon is delivering analytics solutions, creating models, and running AB testing. Depending on the business, team, and project, the core focus will vary from one data science role to another.

For instance, a data scientist in the Alexa — Onboarding team may focus on devising customer success metrics. Another data scientist in the shopping experience team may predominantly run experimentations to optimize the shopping experience among users. Finally, a data scientist in the AWS team may function as a consultant who may build custom model solutions for AWS enterprise clients.

The bottomline is that the role is just one attribute that defines the type of work you will be doing. You need to consider related attributes such as the team and business you will be placed under.

Below are the key responsibilities and qualifications for the data scientist role at Amazon. Note that the details vary by level and team.

Key Responsibilities

Build models using machine learning, statistical modeling, probability, and other quantitative techniques.

Design, run and evaluate an AB test to optimize the user experience.

Design metrics that measure user behaviors such as onboarding, engagement and churn.

Build dashboards with key metrics for business stakeholders.

Ability to make sense of business problems and messy data sets.

Adapting to the latest modeling techniques.

Competencies in SQL and programming languages such as Python and R.

Evaluate models and improve baseline solutions by new data signals and modeling techniques.

Coordinate with researchers, software engineers, and business stakeholders to frame a vague problem into a specific objective.

Communicate clearly in writing and speaking to both technical and non-technical partners.

Basic Qualifications

Bachelor’s Degree

2+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)

2 years working as a Data Scientist

Preferred Qualifications

Master’s degree or PhD in computer science, statistics, information systems, economics, mathematics, or similar

Strong proficiency in SQL and coding languages (i.e. R, Python)

2+ years of industry experience working with large-scale, complex datasets to create machine learning solutions for optimization, forecasting and/or fraud detection.

2+ years of industry experience in data analytics roles involving data extraction, analysis, and communication.

Strong competency in statistical modeling such as linear and logistic regression models.

Strong verbal and writing skills when communicating with technical and non-technical stakeholders

Experience in supervised models and/or unsupervised clustering.

Direct experience analyzing A/B experiments

Demonstrated record in identifying project goals and direction under ambiguity.

Machine Learning Engineer

The machine learning engineer role at Amazon is similar to the ones seen in other FAANG companies. In general, ML engineers utilize expertise in software engineering, machine learning, and statistics to build and deploy machine learning models. In addition, they focus on building scalable modeling pipelines that support server requests efficiently.

As an ML engineer at Amazon, you are plugged into a product area, collaborating with data scientists, software engineers. and business stakeholders in creating and launching production models. Given the emphasis on software engineering, ML engineers often have computer science backgrounds and knowledge in deep learning algorithms. Teams that actively recruit ML engineers are shopping, Alexa, and AWS.

Below are the key responsibilities and qualifications for the machine learning engineer role at Amazon. Note that the details vary by level and team.

Key Responsibilities

Build and productionize models using machine learning, statistical modeling, probability, and other quantitative techniques.

Design, run and evaluate an AB test to optimize the user experience.

Understands distributed computing framework (e.g. Spark).

Design and build text-based storage and indexing systems in large distributed computing environments.

Coordinate with data scientists, researchers, software engineers, and business stakeholders to frame a vague problem into a specific objective.

Communicate clearly in writing and speaking to both technical and non-technical partners.

Basic Qualifications

Ph.D. or M. SC + 4 years of industry experience in computer science, machine learning, or related discipline.

Depth and breadth in state-of-the-art computer vision, recommender system, search, and other machine learning technologies.

Experience in deploying, monitoring, and iteratively improving the production machine learning models.

Proven track record of innovation in creating novel algorithms and advancing the state of the art in computer vision and deep learning.

Strong understanding of distributed systems and system designs.

Experience working with real data.

Ability to develop practical solutions to complex problems.

Strong communication and collaboration skills.

Proficiency in programming languages (i.e. Python, Java).

Preferred Qualifications

Ph.D. with multiple years of industry experience in computer science, machine learning, or related discipline.

Experience in deploying, monitoring, and iteratively improving the production machine learning models.

Publications at top-tier peer-reviewed conferences or journals

Understanding of Software Development Life Cycle (SDLC) and project planning/execution skills including estimating and scheduling.

Ability and willingness to multi-task and learn new technologies quickly.

Applied Scientist / Machine Learning Scientist

The applied scientist, or sometimes branded as the machine learning scientist, focuses on research and application of machine learning within a particular product. Like data scientists, they grasp the fundamentals of statistics and machine learning but possess deeper knowledge about advanced ML techniques and algorithms.

One might wonder how the applied scientist role is different from the ML engineer role given that both require strong advanced knowledge in ML. The primary difference is that ML engineers have a broader focus on building scalable pipelines and systems that support ML models while applied scientists focus more on the research and application of the ML models.

For instance, an applied scientist in the SCOT team may build demand prediction models for the inventory management team. Another example could be that an applied scientist in the search team may focus on developing and implementing new search and recommender systems for customer products.

Below are the key responsibilities and qualifications for the applied scientist role at Amazon. Note that the details vary by level and team.

Key Responsibilities

Build and productionize models using machine learning, statistical modeling, probability, and other quantitative techniques.

Research and implement novel machine learning and statistical approaches

Coordinate with data scientists, researchers, software engineers, and business stakeholders to frame a vague problem into a specific objective.

Communicate clearly in writing and speaking to both technical and non-technical partners.

Basic Qualifications

Ph.D. or M. SC + 4 years of industry experience in computer science, machine learning, or related discipline.

Strong foundation in model development, model validation and model implementation.

Experience in deploying, monitoring, and iteratively improving the production machine learning models.

Proven track record of innovation in creating novel algorithms and advancing the state of the art in computer vision and deep learning.

Strong understanding of distributed systems and system designs.

Experience working with real data.

Ability to develop practical solutions to complex problems.

Strong communication and collaboration skills.

Proficiency in programming languages (i.e. Python, Java).

Preferred Qualifications

Ph.D. with multiple years of industry experience in computer science, machine learning, or related discipline.

Experience in deploying, monitoring, and iteratively improving the production machine learning models.

Publications at top-tier peer-reviewed conferences or journals

Ability and willingness to multi-task and learn new technologies quickly.

Research Scientist

The research scientist at Amazon is primarily focused on developing state-of-the-art algorithms in search, recommender systems, computer vision, natural language processing, and artificial intelligence. Equivalent roles in other FAANG companies would be AI researchers at Google and Facebook. Their works have a broader implication on Amazon as a whole, and they publish their work in the form of research papers in ML and AI journals.

Below are the key responsibilities and qualifications for the research scientist role at Amazon. Note that the details vary by level and team.

Key Responsibilities

Take ownership to define business problems, analyze and design solutions for complex problem areas and/or opportunities in existing or new business initiative

Own the delivery of modeling solutions for an entire business application.

Evaluate cross-team perspectives, use quantitative methods to derive justification, and build consensus on a roadmap on the required level of analyses to meet a goal.

Understand how easily a recommended solution can be implemented in a production software system and/or operational process.

Apply advanced scientific methods and principles, mathematical theory and/or statistical analysis to improve upon existing approaches.

Research the state-of-the-art ML and AI techniques that have broad usage across products.

Basic Qualifications

Ph.D. or M. SC + 4 years of industry experience in computer science, machine learning, or related discipline.

Depth and breadth in state-of-the-art computer vision, recommender system, search, and other machine learning technologies.

Experience in deploying, monitoring, and iteratively improving the production machine learning models.

Proven track record of innovation in creating novel algorithms and advancing the state of the art in computer vision and deep learning.

Strong understanding of distributed systems and system designs.

Experience working with real data.

Ability to develop practical solutions to complex problems.

Strong communication and collaboration skills.

Proficiency in programming languages (i.e. Python, Java).

Preferred Qualifications

7+ years of hands-on experience applying theoretical models in an applied environment

Significant peer-reviewed scientific contributions in premier journals and conferences

Proven ability to work effectively in a cross-functional team

Ability to work on a diverse team or with a diverse range of coworkers

2. Interview Process

Recruiter Call (30 Minutes)

The initial meeting with Amazon is a 30-minute recruiter screen, which is designed to screen the role-fit, culture-fit and logistics of the candidate.

Before the call

The recruiter sees your job application that includes a resume and an optional cover letter in the applicant tracking system (ATS). Your application is algorithmically ranked based on how well your candidacy matches the roles described in the job posts. Recruiters will typically prioritize applications with higher ranking to contact first.

During the call

During the call, which is about 20 to 30 minutes, the recruiter will format the meeting in the following structure:

Introduction — The recruiter will describe more details about the role expectation and team.

Candidate background — This is a chance to share your story. The recruiter will ask, “Tell me about yourself.” You can provide a high-level description of your academic and career backgrounds. Some follow-up questions include: “Why do you want to work for Amazon?”

Logistics — The recruiter usually asks the following: Where are you located? Are you a U.S. citizen? If not, do you need an employer sponsorship for your visa? What are your availabilities for technical interviews?

Follow-Ups — The recruiter will detail the next steps in terms of when you should hear back and technical rounds. This is your chance to ask as many questions as you can to map out the technical interviews end-to-end. The more information you have, the more you can leverage it to prepare for interviews.

After the call

After the call, the recruiter will follow up with the hiring manager with notes gathered about the candidate’s background, technical screening, logistics, and culture fit. If the recruiter and hiring manager believe that you have potential, then they will advance you to the first technical round.

Preparation Tips

To demonstrate a really good impression, make sure you prepare the following:

Create a short elevator pitch explaining why you want to work for Amazon.

Project a friendly and positive impression during the call.

Prepare questions to ask in advance. For instance, ask questions that will help you gather as much information about the interviews as possible: how many rounds? What is the type of each round? Who is the interviewer? Gathering this information can help you design a prep strategy.

Phone Screening (45 to 60 Minutes)

The aim of the technical phone screen is to assess the candidate’s communication ability and technical aptitude in coding, SQL, statistics and/or machine learning. Just like other tech companies, the technical screen is 45 to 60 minutes, and it is conducted by a hiring manager or senior data scientist Depending on the role, level, and team, the interview questions vary. In general, you can expect questions from a combination of topics including past project experience, coding, SQL, statistics, machine learning and/or Amazon’s leadership principles.

For instance, a phone screen could be structured as:

5 Minutes — Introduction 20 Minutes — Python coding 15 Minutes — ML breath questions or a case problem 5 Minutes — Q&A with the interviewer

Another example could be:

5 Minutes — Introduction 10 Minutes — Python coding 10 Minutes — SQL 15 Minutes — ML + Statistics breath questions 5 Minutes — Q&A with the interviewer

In addition to the variability in topics covered in a phone screen, there are cases when a candidate has two rounds of phone screens before an on-site. For instance, a candidate could end up in the following process:

Phone Screen Round 1

5 Minutes — Introduction 20 Minutes — Python coding 15 Minutes — ML breath questions or a case problem 5 Minutes — Q&A with the interviewer

Phone Screen Round 2

5 Minutes — Introduction 20 Minutes — Discuss previous projects and research 15 Minutes — Amazon leadership principles 5 Minutes — Q&A with the interviewer

The bottomline is that, when interviewing for data-roled at Amazon, expect variability in the question topics and number of phone screens. So, coordinate with an Amazon recruiter to get as much information as possible about the interview process, and cover as much breath leading up to the phone screen.

Preparation Tips

In general, for the preparation tip, here’s what you need to do:

Practice SQL questions Practice coding questions Prepare for LP Cover breadth on statistics and machine learning

For more Amazon interview questions, check out datainterview.com!

On-Site Rounds (5 to 6 Hours)

The on-site at Amazon is the hardest stage of the interview process. You will be on 45-to 60-minute interviews back-to-back with a short break. The interviews, as a whole, touch on various topics from statistics, SQL, coding, machine learning and leadership principles.

To thrive in the on-site stage, the ability to retain focus and remain calm under pressure is required.

Let’s overview the rounds in the on-site stage. Note that the rounds and questions vary given the role (data scientist vs MLE vs applied scientist vs research scientist). Lastly, for more details, on each of the question types, jump to the Question Types section.

Machine Learning — Breath, depth, and case-based questions on machine learning, deep learning. Depending on the roles, the questions could be domain-specific. For instance, the MLE — Search or Applied Scientist — Search could be asked on NLP and learning to rank theories and case questions (i.e. How would you solve a cold start problem?). Statistics — Breath, depth, and case-based questions on statistical theory, probability, regression modeling, and experimentation. Data scientists should hone in on the fundamentals of statistics. Expect breath-style questions such as “What is the p-value?” and case-style questions that may involve designing experimentation for Amazon’s product. Coding — Expect algorithms and data structure problems especially for MLE roles. For data scientists, you can expect easy algo. questions that require string and/or integer manipulations. In addition, expect data manipulation questions that require you to use vanilla code or a third-party library to wrangle a table. These question styles are similar to SQL table manipulation given that you are using a programming language of your choice, such as Python and R. SQL — The SQL round is quite common and standard across all FAANG interviews. Essentially, you are given a set of tables and one to three problems to solve using common SQL clauses such as JOINS, WHERE, and GROUP BY and window functions such as AVG and SUM. Leadership Principles — This is Amazon’s behavioral round. Essentially, you need to convey compelling stories that convey the leadership principles at Amazon. For more details jump to the Question Types section.

In terms of how the rounds are structured, here are examples:

Example 1 — Data Scientist Role

Coding + SQL Statistics — Breath/Depth/Case ML — Breath/Depth/Case Leadership Principles Leadership Principles

Example 2 — MLE Role

Machine Learning — Breath + Leadership Principles Machine Learning — Case + Leadership Principles Data Manipulation Coding + Leadership Principles Algorithms & Data Structure Coding SQL Manipulation + Leadership Principles

Preparation Tips

To demonstrate a really good impression, make sure you prepare the following:

Brush up on statistics and machine learning fundamentals. Create a deck of index cards with key concepts. Be able to explain each concept with completeness and brevity.

Practice mock interviews by pairing up with study buddies or FAANG instructors at datainterivew.com.

Remember Amazon’s leadership principles and practice telling stories that convey the attributes.

Anticipate questions you will be asked during the interview. Work with the recruiter to get key information such as the # of rounds, interview types, and general preparation tips.

3. Question Types

Machine Learning

The machine learning questions can be categorized into three types — breadth, depth, and an applied case.

Breath-style questions

What is the variance-bias trade-off? What is the curse of dimensionality? How do you perform feature selection? What is the random forest model?

Depth-style question

Suppose you increase the minimum sample size per leaf in terminal nodes, what happens to the variance and bias of the random forest model? Explain the difference between the ADAM optimizer and gradient descent. Explain the difference between RESNET vs CNN.

Applied case problem

Design a recommender system for the Amazon grocery app? How would you solve a cold-start problem in a recommender system? How would you build a forecasting model?

For data scientists: Expect some fundamental questions breath-style and depth-style statistical learning problems like logistic and linear model fundamentals. Don’t be too concerned with deep learning unless you mention this on your resume. You won’t be tested on it.

For ML engineers / applied scientists / research scientists: Expect deep-dive questions on machine learning theory and applications. In addition, whatever you place on your resume + role requirement, expect questions on the topic. For instance, if you apply for a team that uses computer vision, and your resume mentions it, expect questions on the topic.

Statistics

Fundamentals of statistics are required to perform well across all the data roles at Amazon. Expect “product-style” interview questions in the statistics portion that asks how to design an AB test. Similar to the ML question types, expect coverage in-breath, depth, and applied case problems. Once again, the role defines the type of questions you should expect:

Breath-style question

What is the Simpson’s Paradox? What is the p-value? What is the logistic regression model?

Depth-style question

What are the differences among type I, type II, and type III error rates? If an outlier is present, how does this affect the assumption of a linear model?

Applied-case problem

How would you build an onboarding metric for Amazon shoppers? How would you design an AB test on a marketing campaign?

For data scientists: Definitely expect one or two rounds focused on statistics. Expect all the styles of questions. Brush up on the fundamentals and applications of statistics, probability and regression modeling.

For ML engineers / applied scientists / research scientists: Unless the role requires AB testing, you may or may not encounter a statistics-specific round. You could expect breath-style questions on statistics. But, in terms of depth and applied-case style questions, expect such questions on ML theory and applications, less on statistics. The only exception is that you do have a statistics round.

Coding

Expect algorithms and data structure problems especially for MLE roles. For data scientists, you can expect easy algo. questions that require string and/or integer manipulations. In addition, expect data manipulation questions that require you to use vanilla code or a third-party library to wrangle a table. These question styles are similar to SQL table manipulation given that you are using a programming language of your choice, such as Python and R.

Algorithms and Data Structures — Leetcode-style questions Statistical Coding — A problem that requires you to write a statistical function such as, given a list of actuals and predicted values, computing the MSE, MAPE, and such. Table Manipulation — SQL style questions

For all roles, expect a combination of the three question styles mentioned.

SQL

The SQL round is quite common and standard across all FAANG interviews. Essentially, you are given a set of tables and one to three problems to solve using common SQL clauses such as JOINS, WHERE, and GROUP BY and window functions such as AVG and SUM.

Leadership Principles

This is Amazon’s behavioral question. Essentially, you need to convey compelling stories that convey the leadership principles at Amazon. General tip — remember all the principles and create one or two stories that convey the attributes.

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.” As 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 judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs. 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. 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 anecdotes 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.

Where can you find more practice problems?

For more prep content, check out datainterview.com :)

There are advanced tips on how to prepare for data science interviews and land your dream job at top companies such as Facebook, Amazon, Apple, Netflix and Google.

The flagship product, the monthly subscription course (updated every month), contains the following core features:

Case in Point — 40 data science case problems and solutions AB Testing Course — 12+ lessons on AB testing Mock Interview Videos — 4x1-hour recordings of mock interviews based on technical screenings at top companies. Question Bank — A list of statistics and ML questions commonly asked in interviews. SQL Drills — SQL problems and solutions to help you ace SQL rounds. Slack Study Group — Network with a community of job candidates and data science instructors who work in FAANG companies.

P.S.

Here are additional resources that can be helpful for your prep :)