Amazon Data Scientist Interview Questions
Amazon Data Scientist interview questions are famously comprehensive because Amazon evaluates both technical depth and Amazonian fit. Expect a mix of SQL and Python problems, statistics and experiment-design questions, machine‑learning discussion, and behavioral probes tied to Amazon’s Leadership Principles. Interviews typically include an initial recruiter screen, one or two technical phone screens, and a loop of 4–6 on‑site/virtual interviews where each 45–60 minute slot focuses on a different competency. Interviewers look for clear problem decomposition, metric-driven thinking, defensible trade‑offs, and the ability to translate analysis into business impact. For effective interview preparation, build a structured plan: craft concise STAR stories mapped to Leadership Principles with quantified outcomes, drill SQL (joins, window functions, CTEs, performance), refresh statistics and A/B testing fundamentals, and sharpen Python/data-manipulation skills. Practice explaining assumptions, communicating results for technical and non‑technical audiences, and walking through model choices and evaluation metrics. Mock interviews and timed problem sets that simulate the loop rhythm are especially valuable to convert knowledge into polished, confident answers.
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"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
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Answer core probability and inference questions
You are interviewing for a Data Scientist role. Explain/derive the following statistics fundamentals. 1. State the Central Limit Theorem (CLT). What c...
Prioritize Tasks and Respond to Coworker Concerns
Prioritize Tasks and Respond to Coworker Concerns Task Prioritization and Coworker Response Simulation (Data Scientist) Context You are a Data Scienti...
Build DID panel and compute effects in SQL
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Design an end-to-end spam detection system
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Diagnose and fix underperforming ML model
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Calculate A/B sample size, CI, decision rules
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Prioritize a new warehouse proposal with data
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Optimize precision–recall under class imbalance
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Prove and apply statistical ML fundamentals
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Walk through an A/B test end-to-end
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Explain core ML concepts and metrics
You are interviewing for a Data Scientist role. Answer the following ML fundamentals questions clearly and concisely. Concepts 1. Explain the bias–var...
Identify Issues and Redesign Customer-Conversion Chart
Identify Issues and Redesign Customer-Conversion Chart Critique and Redesign a Customer-Conversion Visualization Context Assume you are reviewing a ch...
Design a robust traffic forecasting pipeline
Forecasting Daily Amazon Retail Traffic: End-to-End Design You are given 5 years of daily Amazon retail site traffic counts. Design an end-to-end fore...
Handle scope creep and teammate conflict
Behavioral & Leadership Two-Part Prompt (Data Scientist — Technical Screen) You will answer two behavioral prompts relevant to a data scientist role. ...
Find top-spend categories per customer with ranking
Using the schema and sample data below, write a single ANSI SQL query (CTEs allowed; no temp tables) that returns, for each customer, their top 2 prod...
Verify subscriptions and analyze orders with SQL/Python
You are given two tables. Write SQL and Python (pandas) to answer the sub-questions precisely, handling edge cases, ties, and missing data. Schema - s...
Evaluate concession gift-card policy with DID
Evaluate a Gift-Card Concession Pilot (Causal Impact with Staggered Adoption) Context Several regions piloted a policy: when a shipment is lost or dam...
Demonstrate ownership and communication under pressure
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Implement list overlap and dense-ranked word frequencies
Part A — Multiset overlap count Implement count_overlap(a: List[int], b: List[int]) -> int that returns the size of the multiset intersection of a and...