Amazon Data Scientist Interview: Questions, Leadership Principles & Process (2026)
5 min read13 practice questionsUpdated Sep 29, 2026
Data Scientist interviews test how you pick a method, check your assumptions and turn a result into a decision. This guide gives behavioral, technical and case study practice questions for the Amazon loop, with notes on how to answer them.
Hear how your answer lands before the interview
“Tell me about a time you Dove Deep into a dataset and discovered something non-obvious that changed a business decision.”
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Sample Amazon Data Scientist Interview Questions
Behavioral Questions
6 questions- 1
Tell me about a time you Dove Deep into a dataset and discovered something non-obvious that changed a business decision.
Mid-levelHow to answer: Use STAR with specific numbers. Show your investigation process: what made you suspicious, how you dug deeper, what you found, and the business impact. LP: Dive Deep + Deliver Results.
- 2
Describe a time you had to deliver results on a data project with incomplete or poor-quality data.
Mid-levelHow to answer: Show resourcefulness: how you identified the data gaps, what imputation or augmentation strategies you used, how you communicated limitations clearly, and what the outcome was. LP: Deliver Results + Invent and Simplify.
- 3
Tell me about a time you pushed back on a stakeholder's request because the analysis approach they wanted was flawed.
SeniorHow to answer: Show principled thinking: you identified the flaw, communicated it diplomatically with evidence, proposed an alternative approach, and built agreement. LP: Have Backbone; Disagree and Commit + Insist on Highest Standards.
- 4
Describe a time you took full ownership of a data science project from problem definition to business impact — including parts outside your direct responsibility.
Mid-levelHow to answer: This is a core Ownership LP question. Show end-to-end thinking: you didn't just build a model and hand it off — you defined the metric, built the pipeline, partnered with engineering for deployment, and tracked business outcomes. LP: Ownership + Bias for Action.
- 5
Tell me about a time you simplified a complex analytical approach to make it more practical for your team or stakeholders.
Mid-levelHow to answer: Show elegance in simplification: you started with a complex solution, identified unnecessary complexity, and delivered something simpler that achieved the same outcome. Quantify the efficiency gain. LP: Invent and Simplify.
- 6
Give me an example of when you obsessed over a customer or end-user problem and went beyond what was expected to solve it with data.
SeniorHow to answer: Demonstrate Customer Obsession: you proactively identified a user pain point through data, designed an analysis nobody asked for, and drove a tangible improvement. Show working backwards from the customer. LP: Customer Obsession + Earn Trust.
The six topics behind behavioral questions, and how to prep a story for each →
Technical Questions
6 questions- 1
Write a SQL query to identify Amazon customers who made at least 3 purchases in their first 30 days and compare their 6-month LTV against customers who made fewer purchases.
Mid-levelHow to answer: Compute first purchase date per customer, filter to those within 30 days, count purchases, join with 6-month revenue table, and compute LTV averages for each cohort. Use CTEs for clarity.
- 2
Write a SQL query to calculate the week-over-week growth rate of new Prime subscribers for the last 12 weeks.
Mid-levelHow to answer: Join weekly sign-up counts with a LAG() window function to get prior week count. Compute (this_week - last_week) / last_week as growth rate. Handle week boundaries correctly.
- 3
How would you design an experiment to measure whether free same-day delivery increases customer lifetime value?
SeniorHow to answer: Discuss: randomization unit (customer), treatment/control assignment, primary metric (6-month LTV or repeat purchase rate), guardrail metrics (profit margin, warehouse cost), experiment duration (accounting for delayed LTV signal), and how you'd handle contamination between user groups.
- 4
Build a model to predict which Amazon customers are likely to cancel Prime membership in the next 60 days.
SeniorHow to answer: Define churn precisely (non-renewal at billing date). Feature engineering: usage frequency, feature adoption, last active date, purchase frequency trend, support contacts, streaming/free delivery usage. Discuss model choice, evaluation metrics (AUC, precision/recall trade-off for intervention targeting), and how to use predictions for retention campaigns.
- 5
How would you measure the incremental impact of Amazon advertising on product sales, controlling for organic search?
SeniorHow to answer: Discuss the attribution problem: how do you separate ad-driven purchases from organic? Methods: matched pair holdout experiments, multi-touch attribution models, difference-in-differences with natural experiments, or media mix modeling. Show awareness of attribution limitations.
- 6
How would you build a demand forecasting model for Amazon Fresh inventory in a new geographic market?
SeniorHow to answer: Cover: cold start problem (no historical data), use of proxy markets (similar demographics), time-series model selection (SARIMA, Prophet, or gradient boosted trees with lag features), demand signals (weather, holidays, local events), model evaluation (MAPE, RMSE), and refresh cadence.
Case Study Questions
1 question- 1
Amazon's search click-through rate dropped 12% in a specific product category last month. How would you investigate?
SeniorHow to answer: Structure: data integrity check first, then segment by device, marketplace, query type, product subcategory. Check experiment log for recent launches. Look at ranking model changes, inventory levels, and competitor pricing. Form ranked hypotheses with data to support each.
Rehearse this one out loud:
“Tell me about a time you Dove Deep into a dataset and discovered something non-obvious that changed a business decision.”
Preparation Tips for Amazon Data Scientist Interviews
- Prepare 8-10 strong STAR stories mapped to Amazon's Leadership Principles — Dive Deep, Deliver Results, Customer Obsession, Ownership, and Invent and Simplify are most tested for DS roles.
- Every STAR story must include specific numbers: revenue impact, percentage improvements, team size, timeline. The Bar Raiser will press you for quantified results.
- Master SQL window functions and complex aggregations — Amazon SQL interviews are well-known for difficulty, with multi-step analytical queries on Redshift.
- Practice articulating business impact, not just model accuracy: 'My churn model improved retention by X%' not just 'My model achieved 0.85 AUC'.
- Map each of your STAR stories to 2-3 Leadership Principles so you can reuse them flexibly — interviewers ask about different LPs and you need coverage across all key ones.
- Understand Amazon's business context: Retail, AWS, Advertising, Alexa, and Prime are very different business units with different DS priorities.
- Study causal inference methods — Amazon increasingly asks senior DS candidates about uplift modeling, A/B testing at scale, and treatment effect estimation.
- Know Amazon's recommendation system basics: collaborative filtering, content-based filtering, and how Amazon handles the cold start problem.
- The Bar Raiser can veto your hire even if all other interviewers say yes — their LP evaluation carries outsized weight, so ensure your stories show genuine personal ownership, not team accomplishments.
- For the behavioral round, use the 'flywheel' technique: show how one LP-aligned action triggered a chain of positive outcomes. For example, Diving Deep into data led to an Insight that Delivered Results for the Customer.
Frequently Asked Questions - Amazon Data Scientist
Amazon DS interviews include: recruiter screen (30 min), technical phone screen covering SQL or ML basics (45-60 min), and an onsite loop (virtual) with 4-5 rounds: SQL and data analysis, statistics/ML, case study or take-home project, behavioral (Leadership Principles), and sometimes a product sense or business case round. Every interview includes LP behavioral questions woven throughout. The Bar Raiser participates in one round.
Extremely important — potentially more than at any other company. Every interviewer asks LP-based behavioral questions, and your answers are shared and debated in the debrief. The most relevant LPs for DS roles are: Dive Deep (rigorous analysis), Data-driven decision making (Customer Obsession, Insist on Highest Standards), Ownership (taking end-to-end responsibility for analytical projects), Deliver Results, and Invent and Simplify (finding elegant solutions to complex analysis problems).
Advanced SQL is required: complex joins, window functions (LAG/LEAD, RANK, CUMULATIVE SUM), CTEs, subqueries, date/time functions, and query optimization. Amazon typically uses Redshift (SQL syntax similar to PostgreSQL). Common problems involve customer behavior analysis (purchase funnels, retention), inventory and supply chain analytics, advertising attribution analysis, and product engagement cohort analysis.
Amazon DS interviews cover: supervised learning fundamentals (regression, classification, tree-based models), model evaluation (AUC, precision-recall, confusion matrix, cross-validation), feature engineering, overfitting and regularization, recommendation systems (Amazon's core product), demand forecasting (time series), and experiment design. Senior roles also cover causal inference, uplift modeling, and large-scale ML system design.
A Bar Raiser is an Amazonian from outside your hiring team who participates in your interview loop to ensure hiring decisions raise Amazon's overall talent bar. They focus on Leadership Principles and can veto a hire even if the rest of the panel is positive. For DS roles, the Bar Raiser typically asks behavioral LP questions — your STAR stories must be specific, measurable, and show clear personal ownership and impact.
Pay varies by level, location and team. Many postings list a base-pay range (US postings in states such as California and New York are required to), so check the posting for your role and ask your recruiter how equity and any bonus work at your level.
Amazon take-homes typically ask you to: analyze a dataset and derive business insights, build and evaluate a predictive model, or design an experiment to test a business hypothesis. Focus on: clearly framing the business problem, explaining your analytical choices, identifying data limitations honestly, and connecting findings to actionable business recommendations. Presentation matters — structure your write-up for a business audience, not just a technical one.
Three factors: (1) The LP behavioral component is as important as technical skills — weak LP examples can override strong technical performance. (2) The breadth of technical evaluation — SQL, statistics, ML modeling, and business acumen are all tested in a single loop. (3) The Bar Raiser adds an independent quality check. Candidates who excel technically but can't articulate their LP-aligned impact often don't pass Amazon's bar.
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