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.

Amazon DS compensation (2025 data): DS I (L5): $140k-$185k base, $280k-$420k total (with sign-on and RSUs); DS II (L6): $170k-$225k base, $380k-$550k total; Senior DS (L7): $210k-$280k base, $500k-$750k total. Amazon's base salaries are capped at $350k company-wide — additional compensation comes through RSUs (4-year vest) and cash sign-on bonuses for high-demand roles.

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.