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Amazon
Amazon Data Scientist Interview Guide 2026
Complete Amazon Data Scientist interview guide. Learn about the interview process, Leadership Principles, SQL, statistics, ML, and experimentation rounds for Amazon DS roles.
5 min read
Updated Jan 2026
232+ practice questions
232+
Practice Questions6
Rounds6
Categories5 min
ReadTL;DR
Amazon's Data Scientist interview in 2026 combines rigorous technical assessment with deep behavioral evaluation through Leadership Principles. The process typically includes an online assessment, a phone screen, and a 4-5 round onsite loop. Technical rounds cover SQL, statistics, machine learning, and case studies. Every round also includes LP behavioral questions. Amazon DS roles are highly applied. You'll be building models and running experiments that directly impact business metrics like revenue, conversion rates, and customer experience. The Bar Raiser system applies here too. Expect 4 to 6 weeks from first contact to offer.
4-6 weeks
232+ questions
Sample Questions
232+ in practice bank
Design an A/B test for a new checkout flow on Amazon
Amazon is testing a new one-click checkout flow. Design the experiment: define primary and guardrail metrics, determine randomization unit, calculate required sample size, and explain how you'd handle cannibalization effects.
A product category shows a 20% revenue drop. Diagnose the cause.
Walk through a structured diagnostic framework. How would you decompose revenue into components? What data would you pull first? How would you differentiate between seasonal effects, competitive pressure, and product issues?
Write SQL to find the top-selling products by category and region
Given tables for orders, products, and regions, write a query using window functions to rank products within each category-region pair by revenue.
Write SQL to calculate month-over-month growth rates
Given a daily revenue table, write a query to compute monthly totals and month-over-month growth percentages using window functions.
Explain how you'd build a customer churn prediction model
Walk through end-to-end model development: feature engineering from behavioral data, model selection, handling class imbalance, evaluation metrics, and how you'd integrate predictions into a retention campaign.
How would you build a demand forecasting model for Amazon inventory?
Design a forecasting system for Amazon's inventory. Discuss feature engineering (seasonality, promotions, external signals), model choices (time series vs. ML), and how you'd handle cold-start products.
When is a correlation misleading?
Give examples of spurious correlations, confounding variables, and Simpson's paradox. Explain how you'd identify and address these issues in a real analysis.
Explain the assumptions of linear regression and when they break
Cover linearity, independence, homoscedasticity, and normality of residuals. For each assumption, give a real-world example of when it fails and how you'd detect and address the violation.
Tell me about a time you used data to change a business decision
Customer Obsession and Dive Deep LPs. Describe how you identified an insight, presented it to stakeholders, and influenced a concrete business outcome with specific metrics.
Tell me about a time you delivered results under a tight deadline
Deliver Results LP. Describe the constraints, what trade-offs you made, and the quantifiable impact. Amazon values candidates who ship despite obstacles.
About the Interview Process
Amazon's DS interview follows the same LP-driven structure as SDE interviews but replaces coding algorithms with SQL, statistics, and ML rounds. Every round pairs technical questions with Leadership Principle behavioral questions. A Bar Raiser with veto power is part of the loop.
Online Assessment
SQL problems and/or a data analysis case study, plus a work simulation or LP assessment. The SQL problems test joins, aggregations, and window functions. Complete all sections to advance.
Phone Screen
Mix of SQL coding and statistics questions, plus one or two LP behavioral questions. The interviewer may also ask you to walk through a past data science project.
Onsite: SQL & Analytics
Advanced SQL problems involving complex joins, CTEs, window functions, and data quality handling. You'll work on Amazon's platform or a shared editor. LP behavioral questions follow.
Onsite: Statistics & Experimentation
Hypothesis testing, A/B test design, confidence intervals, and causal inference. Amazon tests practical experimentation knowledge since DS teams run thousands of experiments. LP questions follow.
Onsite: ML & Modeling
Model selection, feature engineering, evaluation, and practical ML trade-offs. Amazon DS roles are applied, so expect questions about deploying models to production and measuring business impact. LP questions follow.
Onsite: Bar Raiser
Deep dive into Leadership Principles, often with a technical case study component. The Bar Raiser ensures you meet Amazon's hiring bar across both technical and behavioral dimensions.
Timeline
4 to 6 weeks from online assessment to offer. Amazon's process tends to be faster than Google or Meta.
Tips
Prepare LP stories that involve data-driven decision making. Amazon loves seeing DS candidates who use data to influence business outcomes.
Practice SQL on a platform without auto-complete. Amazon's assessments don't provide IDE features.
For experimentation, know how to calculate sample sizes, handle multiple comparisons, and deal with interference between treatment groups.
The Bar Raiser will probe your LP stories deeply. Have backup stories ready if they push for more detail.
Amazon DS roles are business-oriented. Frame your answers in terms of customer impact and revenue, not just model accuracy.
What Amazon values in Data Scientists
Amazon's DS culture is intensely customer-obsessed and metric-driven. The company expects data scientists to go beyond building models. You need to frame problems in terms of business impact, design experiments that directly affect customer experience, and communicate findings to non-technical leaders.
SQL skills are foundational. Amazon's data infrastructure is massive, and you'll spend significant time querying large datasets. The interview tests your ability to write complex queries efficiently and correctly under pressure.
Experimentation is core to how Amazon makes product decisions. You'll need to understand A/B testing rigorously, including edge cases like interference, novelty effects, and how to handle multiple metrics that move in different directions.
Applied ML at Amazon
Amazon's DS roles are heavily applied. You might work on demand forecasting, fraud detection, search relevance, recommendation systems, or customer segmentation. The ML interview round focuses on practical skills rather than theoretical depth.
Expect questions about the full ML lifecycle: defining the problem, choosing features, selecting models, evaluating performance, and deploying to production. Amazon cares especially about how you'd monitor a model after deployment and iterate based on business metrics.
For case studies, think about problems Amazon actually faces: predicting delivery times, optimizing warehouse inventory, personalizing product recommendations, or detecting fake reviews. Ground your answers in Amazon's specific context when possible.
Leveling & Compensation
| Level | Title | YoE | Total Comp (USD/yr) |
|---|---|---|---|
DS I | Data Scientist I | 0-2 yrs | $130k - $215k |
DS II | Data Scientist II | 2-5 yrs | $195k - $350k |
Senior DS | Senior Data Scientist | 5-10 yrs | $290k - $510k |
Principal DS | Principal Data Scientist | 10+ yrs | $420k - $760k |
Data Scientist I
Solid SQL and statistics skills. Can run and analyze experiments with guidance. Communicates findings clearly to the team.
Data Scientist II
Independently designs experiments and builds models. Identifies key metrics and drives data-informed product decisions. Mentors junior team members.
Senior Data Scientist
Leads analytical strategy for a business area. Defines measurement frameworks and influences product roadmap through data insights.
Principal Data Scientist
Sets data science strategy across an organization. Influences VP-level decisions with data. Recognized as a domain authority.
How to Stand Out
Behavioral Focus Areas
Customer Obsession: using data to understand and improve the customer experience
Ownership: taking accountability for end-to-end project outcomes, not just your piece
Dive Deep: getting into the details of data quality, methodology, and root cause analysis
Deliver Results: shipping analyses and models that drive measurable business impact
Bias for Action: moving quickly with imperfect information rather than waiting for perfect data
Have Backbone, Disagree and Commit: standing by your analysis even when stakeholders push back
1.
Amazon LP stories should include specific metrics. 'I identified a $2M revenue opportunity' is the type of specificity they want.
2.
Practice SQL daily. Amazon's SQL questions are practical and test real-world data manipulation skills.
3.
For experimentation, know how Amazon's scale affects test design. Think about issues like geographic variation and prime vs. non-prime segments.
4.
Frame ML answers in terms of business outcomes, not just model metrics. Amazon cares about ROI, not just AUC.
5.
Prepare for the Bar Raiser by having multiple LP stories for each principle. They'll dig deeper than other interviewers.
6.
Know the difference between correlation and causation cold. Amazon interviewers test this in multiple ways.
Recommended Resources
Ace the Data Science Interview by Nick Singh & Kevin Huo
Trustworthy Online Controlled Experiments by Kohavi, Tang & Xu
Amazon Science Blog
FAQ
How technical is the Amazon DS interview compared to the SDE interview?
The Amazon DS interview replaces algorithmic coding with SQL, statistics, and ML. You won't be solving LeetCode-style problems, but you will write complex SQL queries and work through statistical problems on a whiteboard. The LP behavioral component is equally rigorous for both roles.
Do I need to know AWS services for the DS interview?
Not in depth, but basic familiarity helps. Knowing what S3, Redshift, SageMaker, and QuickSight do lets you reference concrete tools when discussing how you'd build data pipelines or deploy models. The interview tests data science fundamentals, not cloud engineering.
How many LP stories should I prepare?
Prepare at least 12-15 distinct stories covering the top 8 Leadership Principles. Each onsite round tests 2-3 LPs, and with 4-5 rounds, you'll need stories for at least 8-10 different LPs. Having 2 stories per LP gives you flexibility if the interviewer probes deeper.
What's the biggest mistake DS candidates make at Amazon?
Underestimating the LP component. Many strong technical candidates fail because their behavioral answers are vague or not structured around specific examples with measurable outcomes. Spend at least 30% of your prep time on LP stories.
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