Amazon Data Scientist Interview Guide

Phone screen. Amazon’s first live interaction is a 30- to 45-minute discussion, typically with a recruiter or hiring manager.

Preparation checklist. Treat screen, take-home, coding, case, behavioral, and onsite work as preparation categories, not Amazon’s published round names. Use them to organize practice across technical analysis, business reasoning, communication, and experience-based answers.

Interview loop. The final and most comprehensive stage contains four to six interviews lasting 45-60 minutes each. Different interviewers assess different aspects of your skills and experience.

Amazon’s 16 Leadership Principles underpin hiring decisions, and STAR, meaning situation, task, action, and result, is the cornerstone of its interviews. A Bar Raiser from outside the hiring team participates in the loop, and the panel holds a debrief after the final interview. Present evidence that is specific, internally consistent, and clear about your own decisions and results.

For Data Scientist interviews generally, prepare across four connected areas:

• Statistics and experimentation: define hypotheses, choose metrics, identify bias and confounding, interpret uncertainty, and explain whether a result supports a decision.
• SQL and coding: translate a business question into data transformations, write readable logic, check edge cases, and validate the output.
• Modeling judgment: establish a baseline, choose an appropriate method, prevent leakage, select evaluation measures, and discuss operational tradeoffs.
• Communication: explain assumptions, findings, limitations, and recommended actions to people who do not work with statistical methods.

For Amazon behavioral discussions, structure each example with STAR and connect it to relevant Leadership Principles. Distinguish team context from your personal contribution, then state the result and what you learned.

Amazon does not publish a Data Scientist-specific take-home, coding, case, or loop breakdown. Its published formats for neighboring roles should not be transferred to this role.

For Software Development Engineer roles, Amazon describes an online assessment with a coding test. The SDE II version includes 90 minutes for two technical questions, 20 minutes of systems design scenarios, and an 8-minute Work Style Survey. Its loop includes four 55-minute interviews, with expectations around syntactically correct code and software systems design.

For Product Manager - Technical roles, Amazon describes a 60-minute technical phone screen divided between Leadership Principles questions and the technical product life cycle, followed by five 55-minute loop interviews. For Applied Scientist roles, it describes a 60-minute technical phone screen, sometimes another screen, and four 55-minute interviews with the science community.

Your Data Scientist preparation should center on drawing defensible conclusions from data: experimental validity, SQL and coding accuracy, model selection, business consequences, and explanations that non-experts can act on.

Amazon does not publish how long the process takes. It typically provides feedback within two business days after phone interviews and within five business days after the interview loop.

From a preparation standpoint, the challenge is breadth and consistency. The loop spans four to six separate conversations, with each interviewer assessing different aspects of your background. Practice moving between technical depth, judgment, communication, and STAR evidence without changing the underlying details of your examples.

Build an evidence bank. Select work examples that show decisions, obstacles, actions, measurable or observable results, and lessons. Map them to the 16 Leadership Principles, but keep each story natural rather than forcing every principle into one answer.

Practice experimental reasoning. Work from a decision to a hypothesis, metric set, design, validity risks, interpretation, and recommendation. State what evidence would change your conclusion.

Rehearse SQL and coding aloud. Clarify the data grain, write readable logic, test edge cases, and describe how you would verify correctness.

Defend modeling choices. Compare a simple baseline with more complex options, then discuss validation, leakage, error patterns, interpretability, and deployment constraints.

Translate the result. Give the conclusion first, explain the evidence in plain language, and separate known findings from assumptions. A mock interview can reveal where an explanation becomes unclear or a STAR story lacks concrete evidence.