Amazon Data Scientist Interview Guide
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Detailed, specific guidance on the Amazon Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage
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Amazon Data Scientist Interview Guide
The Amazon Data Scientist Interview puts more weight on Leadership Principles than almost any other tech company. At most FAANG companies, technical skills carry most of the decision, but at Amazon, Leadership Principles carry significant weight, sometimes as much as the technical rounds themselves. Prepfully candidates who went through Amazon DS interviews told us every interviewer gets assigned 2-3 specific Leadership Principles to evaluate, and the Bar Raiser can reject you even if you crushed the technical rounds.
Prepfully candidates reported that Amazon typically provides feedback within 2 business days after phone screens and 5 business days after onsite interviews. The full process runs 4-6 weeks from application to offer, though Prepfully candidates with referrals have reported timelines as short as 3 weeks.
According to Daliana Liu, Senior Data Scientist at Amazon AWS, the transition to Amazon required shifting focus from model accuracy to business impact. She emphasizes that communication and stakeholder alignment matter as much as technical depth, a theme that runs through Amazon's entire evaluation process.
The Amazon Data Scientist Interview Process
Amazon structures its hiring into 4 stages. Based on reports from Prepfully candidates, the process follows a predictable pattern, though the Bar Raiser round introduces significant unpredictability.
Relevant Guides
Recruiter Screen (20-30 minutes)
Overview
The recruiter assesses basic qualifications, motivation, and initial cultural fit. Expect questions about your background, why Amazon interests you, and what you know about the team or organization you're applying to.
Leadership Principle screening begins here. Recruiters listen for signals of Customer Obsession and Ownership even in casual conversation. One Prepfully candidate mentioned being asked "What do you know about how Amazon uses data science?", a question designed to test whether you've done real homework on Amazon's data science applications or just skimmed the careers page.
If you're interviewing elsewhere too, Prepfully candidates who compared processes said Meta's recruiter chats felt lighter on Leadership Principles than Amazon's, so don't underweight LP signaling here.
Technical Screen (1-2 rounds, 45-60 minutes each)
Overview
Most technical screens run on CollabEdit or Amazon Chime with the interviewer watching every keystroke. Prepfully candidates told us Amazon leans harder on SQL than other FAANG peers.
Prepfully candidates confirmed that most SQL questions involve data stored in multiple tables, requiring JOINs, CTEs, or subqueries combined with window functions. The remaining technical content covers ML theory, statistics, and probability.
Typical technical screen structure:
- SQL query on simulated e-commerce data (15-20 minutes)
- ML concept questions or statistical reasoning (15-20 minutes)
- Behavioral questions tied to Leadership Principles (10-15 minutes)
One candidate shared on Reddit: "The recruiter said it will be more on pandas and numpy, they asked DS&A (data structures and algorithms)." This mismatch between recruiter expectations and actual interview content is common. Ask your recruiter what to prioritize, but prepare for SQL and coding regardless.
SQL performance matters. With thousands of applicants, the SQL screen is heavily weighted. Prepfully coaches told us that candidates who stumble on basic JOINs or window functions often struggle to advance, even with other strengths.
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Onsite Loop (5-6 interviews, 45-60 minutes each)
Overview
The onsite consists of back-to-back interviews evaluating different competencies. Each interviewer focuses on specific Leadership Principles while also assessing technical depth.
Coding/DSA Round: Problems roughly at a LeetCode Medium level, testing data structures, array manipulation, and algorithm implementation. The coding bar for Data Scientists is lower than Software Engineers, but you still need to solve problems correctly and explain your reasoning.
Statistics/Probability Deep Dive: Hypothesis testing, confidence intervals, A/B test design, and Bayesian reasoning. Prepfully candidates faced questions like "What is p-value and how do you interpret it?" followed by increasingly complex scenarios involving multiple comparison corrections and practical significance versus statistical significance.
Machine Learning Interview: Algorithm selection, model evaluation, feature engineering, and trade-off discussions. Expect questions comparing bagging versus boosting, explaining regularization techniques, and designing recommendation systems for Amazon's product catalog.
Case Study/Product Sense: Business problems grounded in Amazon's operations. Prepfully candidates reported prompts such as "Customers are returning products at high rates, investigate and propose solutions" or "Design an A/B test to evaluate a new Prime feature."
Behavioral Rounds (1-2 interviews): Dedicated Leadership Principle evaluation using STAR format. Each interviewer probes 2-3 specific principles with follow-up questions designed to test the authenticity and depth of your stories. Prepfully coaches warned that weak behavioral performance can outweigh strong technical rounds. Many candidates underestimate how much LP responses factor into the final decision.
For candidates also considering retail and e-commerce companies, the Walmart Data Scientist interview covers similar SQL and business case skills, though the behavioral emphasis varies by team and year.
It's not like you have to clear all five rounds. All five will sit for debrief... if the hiring manager will feel the weak points are coachable, they can still hire you. In Google and Meta, my experience is I have to clear all the rounds. But in Amazon it's not like that.
The Bar Raiser Round
Overview
The Bar Raiser is one of the things that makes Amazon's interview process distinct. Amazon created this role, and many companies have since adopted similar models. This interviewer comes from outside your hiring team, receives training focused on calibration across roles and maintaining hiring standards, and has authority to veto hiring decisions even if every other interviewer recommends hire.
Bar Raisers evaluate long-term potential rather than immediate team fit. They assess whether you're "raising the bar" for Amazon overall, not whether you can fill a specific role. The goal is to compare candidates to the median skill level at Amazon for a given craft, ensuring hires meet or exceed that bar. Amazon's philosophy is that this mechanism helps maintain and grow overall employee quality.
Traits Bar Raisers look for:
- Customer Obsession in action, evidence that you prioritize customer needs over internal convenience
- Clear, structured communication under pressure
- Taking responsibility for outcomes, not tasks
- Willingness to challenge decisions respectfully with data
- Evidence of learning quickly and being coachable
- Clear thinking in ambiguous situations
Prepfully candidates report the Bar Raiser round feels different from other interviews. Expect slower pacing with more layered follow-ups, fewer technical questions, and deeper probing into the "why" behind your decisions. Questions dig deeper into your stories, challenge your assumptions, and test whether you can pivot when your initial approach fails.
Prepfully coaches who have conducted Amazon interviews told us that Bar Raisers often ask the same behavioral question multiple times from different angles. This is intentional. They're testing consistency and depth. Each follow-up question peels back another layer of the situation, exposing gaps in generic or rehearsed stories.
How to prepare for the Bar Raiser: Focus on stories with genuine complexity where you made difficult trade-offs. Practice articulating why you made specific decisions, what you'd do differently, and how you handled pushback. Surface-level STAR prep falls apart under Bar Raiser scrutiny.
Amazon Leadership Principles Deep-Dive
Amazon's 16 Leadership Principles function as the cultural foundation for all hiring decisions. For Data Scientists, certain principles receive heavier weight based on role expectations. Interviewers test these through behavioral questions that require specific examples. They're listening for how you demonstrated each principle, not whether you can recite the definition.
Most Critical Leadership Principles for Data Scientists
The principles below tend to receive more emphasis in Data Scientist interviews based on Prepfully coach observations, though the specific focus varies by interviewer and role level.
Customer Obsession: All decisions should be grounded in customer needs. Demonstrate past examples where you advocated for customers based on data insights, even when it conflicted with internal priorities.
Dive Deep: Leaders operate at all levels and stay connected to details. Show examples of auditing assumptions, questioning data quality, and investigating root causes rather than accepting surface-level explanations.
Deliver Results: Focus on key inputs and deliver with quality and timeliness despite obstacles. Share quantified outcomes, not vague claims of improvement.
Bias for Action: Speed and calculated risk-taking matter more than perfect plans. Demonstrate instances where you made quick decisions based on available information and adjusted course as you learned more.
Are Right, A Lot: Leaders have strong judgment and good instincts. Provide examples of successful predictions, data-driven decisions that proved correct, and how you learned when you were wrong.
Have Backbone; Disagree and Commit: Respectfully challenge decisions with data, then fully commit once decisions are made. For example, prepare a story about disagreeing with a senior stakeholder by presenting data that contradicted their assumption, then fully executing their final decision once the team aligned.
STAR Format Requirements
Behavioral answers should follow the STAR framework with quantified outcomes (revenue impact, cost saved, customer complaints reduced, time saved):
Situation: Set context with specific details (team size, timeline, stakes)
Task: Clarify your individual responsibility versus team contribution
Action: Describe specific steps you took with decision-making rationale
Result: Quantify impact with metrics, business outcomes, and lessons learned
Prepfully coaches told us that candidates need 15-20 prepared stories covering different Leadership Principles. Each story should be deliverable in 2-3 minutes, with enough depth to withstand follow-up questions.
Leadership Principle Questions Prepfully Candidates Reported
Customer Obsession:
- Tell me about a time you made something much simpler for customers
Learn and Be Curious / Deliver Results:
- Describe a project that was not successful, what would you do differently?
Bias for Action:
- Describe a scenario where the deadline was earlier than expected
- Tell me about a time you applied judgment when data was not available
Have Backbone; Disagree and Commit:
- Describe when you disagreed with your manager and how you handled it
Career Motivation Questions (not LP-specific, but commonly asked):
- Why data science? Where do you see yourself in 5 years?
- Tell me about a time you presented data findings to stakeholders and how you tailored your communication
Hiring manager will assign leadership principles like two, two for each interview... In the drop down, if I select ownership, I'll get 10 to 12 questions. So randomly I can choose any question
Schedule a 1-1 session with Amazon Data Scientist coaches on Prepfully to get feedback on your Leadership Principle stories before the real interview.
→ Schedule NowTechnical Interview Questions Prepfully Candidates Reported
SQL Questions
SQL is heavily emphasized in Amazon DS technical screens. Prepfully candidates confirm window functions, CTEs, and complex aggregations appear in most interviews. Interviewers test how you break down problems and structure your thinking, not just whether you know SQL syntax.
Questions reported by Prepfully candidates:
- Calculate month-over-month percentage change in revenue using LAG/LEAD window functions
- Identify the top two highest-grossing products within each category using RANK() and PARTITION BY
- Write a query to explain month-to-month user retention rate using cohort analysis
- Find products where order quantity exceeded average across all products using subqueries
- Calculate running totals and moving averages for sales data
What interviewers evaluate: Not simply memorization, but whether you can decompose ambiguous data problems, optimize queries for large datasets, and explain your reasoning while coding.
Machine Learning Questions
Amazon ML questions prioritize practical application over mathematical derivation.
Questions reported by Prepfully candidates:
- Design a recommendation system for Amazon products (full case study covering retrieval, ranking, and evaluation)
- How would you predict customer churn based on purchase history, browsing behavior, and support interactions?
- Explain how you would enhance Amazon's fraud detection capabilities
- How do you manage unbalanced datasets? (Expect follow-ups on SMOTE, class weights, and evaluation metrics)
- Compare bagging versus boosting, when would you use each?
- What VIF values did you use to remove multicollinearity?
According to a candidate on Reddit, interviewers will "grill you about Bayes' Theorem a billion different ways trying to find a gotcha moment." Prepare for in-depth questions on topics that seem straightforward.
Statistics and A/B Testing Questions
Amazon's experimentation culture means A/B testing receives significant interview weight.
Questions reported by Prepfully candidates:
- Design an A/B test for green versus yellow colors on the buy button. What are your hypotheses, metrics, and sample size calculation?
- How would you design an experiment to evaluate Prime Video recommendations ensuring both user satisfaction and business KPIs?
- A disease affects 1% of the population. A test is 95% accurate. If someone tests positive, what's the probability they have the disease?
- What analysis would you run on non-normal A/B test data?
- How would you handle multiple hypothesis testing across 50 variations?
Case Studies and Business Problems
Case studies ground technical skills in Amazon's business context.
Prompts reported by Prepfully candidates:
- Customers are returning products at high rates, investigate and propose solutions
- Analyze factors affecting Prime membership retention
- How would you improve conversion rate on product pages?
- Design a pricing strategy optimization system
- Optimize inventory placement across fulfillment centers
What interviewers evaluate: Problem structuring, metric selection, connecting technical solutions to business outcomes, and communicating recommendations to non-technical stakeholders.
These questions layer multiple concepts together, and the follow-up questions often probe edge cases and trade-offs. Practice with realistic Amazon-style problems before the interview.
You should not make any, even silly mistake in the syntaxes... The functions we are pretty much okay, even if you use your own function names. But don't make some silly mistakes in the syntax.
Practice Amazon data scientist interview questions with Prepfully's question bank, including SQL and ML problems reported by recent candidates.
→ Practice NowHow to Prepare for the Amazon Data Scientist Interview
SQL Mastery (Highest Priority)
Amazon DS interviews test SQL more heavily than other technical skills. Prepfully candidates who received "Hire" ratings from coaches in mocks went on to receive offers consistently, while those who received "No Hire" ratings in mocks received offers only about 3% of the time.
Practice resources: StrataScratch and DataLemur offer Amazon-specific SQL problems closer to interview expectations than LeetCode. Also practice against Prepfully's SQL question bank.
Statistics and A/B Testing
Amazon expects intuition and applied reasoning over mathematical derivation. You should be able to explain why you'd choose a particular test and what results mean for business decisions.
Key preparation areas:
- A/B test design from scratch: hypothesis formulation, sample size calculation, randomization
- Hypothesis testing and confidence interval interpretation
- Type I/Type II errors and practical implications
- Handling non-normal distributions (bootstrapping, Mann-Whitney U-Test)
Machine Learning Fundamentals
Focus on practical application in e-commerce context rather than theoretical depth.
Priority topics:
- Model evaluation metrics appropriate for different business problems
- Handling class imbalance (common in fraud detection, churn prediction)
- Feature engineering for customer behavior data
- Trade-offs between model complexity and interpretability
Eugene Yan, Senior Applied Scientist at Amazon, describes Amazon's end-to-end problem-solving approach: clarifying ambiguous requirements, writing design docs, working cross-functionally, measuring impact via A/B tests, and maintaining systems in production.
Leadership Principles Preparation
According to discussions on Reddit, many candidates spend 90% of prep time on technical skills and only 10% on behavioral, when Amazon weighs them more evenly. Technical skills get you to the onsite; Leadership Principles often determine the offer.
Preparation approach:
- Draft 15-20 STAR stories that map to all 16 Leadership Principles
- Use specific numbers in every outcome, "cut query time from 8 seconds to 2 seconds" instead of vague claims like "improved performance"
- Say your stories out loud and time yourself, aim for 2-3 minutes each
- Think through potential follow-ups. Interviewers ask 3-4 layers deep on every story, so surface-level prep falls apart fast
Master SQL, A/B testing, and ML system design with Prepfully's Data Science Interview Course, 14 hours of content created by senior data science leaders who have coached candidates across Amazon and other top companies.
→ Start Preparing NowCompensation and Levels
Amazon's compensation structure differs significantly from other FAANG companies due to its backloaded RSU vesting schedule. Based on levels.fyi data for US-based roles and Prepfully candidate reports (compensation changes frequently, treat these as approximate ranges):
Level Breakdown
L4 (Data Scientist I) - 0-2 years experience:
- Total compensation: $170K-$195K
- Base: $130K-$145K
- RSUs: $30K-$40K annually
- Bonus: $10K-$15K
L5 (Data Scientist II) - 2-5 years experience:
- Total compensation: $250K-$295K
- Base: $160K-$175K
- RSUs: $80K-$110K annually
- Bonus: $8K-$12K
L6 (Data Scientist III) - 5-10 years experience:
- Total compensation: $370K-$430K
- Base: $185K-$205K
- RSUs: $170K-$210K annually
- Bonus: $12K-$18K
L7 (Principal Data Scientist) - 10+ years experience:
- Total compensation: $580K-$670K
- Base: $225K-$250K
- RSUs: $320K-$380K annually
- Bonus: $35K-$45K
Understanding Amazon's RSU Vesting Schedule
Amazon uses a distinctive 5/15/40/40 vesting schedule that creates significant year-over-year compensation variation:
- Year 1: 5% of RSUs vest
- Year 2: 15% of RSUs vest
- Year 3: 40% of RSUs vest
- Year 4: 40% of RSUs vest
This means 80% of your equity vests in years 3-4. To offset low year 1-2 equity, Amazon provides sign-on bonuses, typically ranging from $20K-$60K depending on level.
This matters when you compare offers. If you get an L5 offer with a $100K annual RSU grant, you receive around $5K in year 1, $15K in year 2, then $40K in both years 3 and 4. If you're comparing against a company with 25/25/25/25 vesting, your Amazon comp in year 1 will be lower than the "total comp" number suggests.
Negotiation Strategy
Focus your negotiation on sign-on bonus. Base salary has hard caps around $160K-$185K for most levels, so there's not much room to move there. RSU grants have more flex, especially if you bring competing offers from Google, Meta, or similar companies.
Prepfully candidates with competing offers from Google, Meta, or other autonomous vehicle companies reported Amazon typically responds with adjustments. Present competing offers in writing.
The Amazon Data Scientist Interview tl;dr
Leadership Principles carry significant weight in Amazon's evaluation, often as much as technical performance. Candidates who ace technical rounds but deliver weak behavioral responses can still receive rejections. Prepare 15-20 STAR stories with quantified outcomes and practice delivering them under time pressure.
SQL is heavily weighted in the technical screen. Most SQL questions involve JOINs, window functions, and CTEs against simulated e-commerce data. Clean execution matters because Amazon uses this round to assess a large volume of candidates. StrataScratch and DataLemur offer better preparation than LeetCode for Amazon's SQL expectations.
The Bar Raiser has veto authority over hiring decisions, even when other interviewers recommend hire. This person comes from outside your team and evaluates long-term potential and LP fit. Generic stories break down fast under Bar Raiser follow-ups. What matters is depth and authenticity, not how polished you sound.
RSU compensation is backloaded through the 5/15/40/40 vesting schedule. Your year 1-2 comp is lower than years 3-4 because only 20% of equity vests in the first 2 years. Sign-on bonuses help close that gap but are not reflected in annual comp figures. When comparing Amazon offers to companies with standard 25/25/25/25 vesting, factor in how the schedule pays out.
The 4-6 week timeline compresses for referred candidates with strong performance and extends when feedback is mixed or leveling requires discussion.