How Data Science Interviews Work at Amazon

Amazon's data science interview is unlike any other in big tech, and the reason is two words: Leadership Principles.

At most companies, the behavioral round is the warm-up — the thing you get through before the "real" evaluation starts. At Amazon, behavioral questions are woven into every single round and carry roughly equal weight to the technical assessment. A candidate who aces the SQL and machine learning rounds but stumbles on Leadership Principles will not get an offer. Full stop.

If that surprises you, it means you haven't prepped for Amazon specifically. And prepping generically is the fastest way to get rejected here.

The process at a glance

Amazon's interview process typically takes about four weeks from first contact to decision. The structure: a recruiter screen, one or two technical phone screens, and a loop of five to six onsite interviews (often virtual), each lasting 45-60 minutes.

Every onsite interviewer is assigned two or three specific Leadership Principles to probe during their session. This means the behavioral evaluation isn't concentrated in one round — it's distributed across the entire day. Even during a technical round, expect your interviewer to pivot to "tell me about a time when..." at some point.

The Bar Raiser

One of your interviewers will be a Bar Raiser — a specially trained evaluator from outside your target team. The Bar Raiser's job is to make sure Amazon doesn't lower its hiring standards, and they have veto power over the hire. You won't be told which interviewer is the Bar Raiser.

In practice, the Bar Raiser tends to push harder on both technical depth and behavioral responses. They're looking for consistency: does this candidate meet Amazon's bar across the board, not just in their strongest area? If you have one weak round, the Bar Raiser is the person most likely to flag it.

This isn't something you can specifically prepare for — just know that one of your interviewers has a higher bar than the rest and act accordingly.

Leadership Principles

Amazon has 16 Leadership Principles, and they take them seriously. Each behavioral question maps to a specific principle. "Tell me about a time you had to make a decision with incomplete data" maps to Bias for Action. "Describe a situation where you disagreed with your manager" maps to Have Backbone; Disagree and Commit.

You need to know these principles before your interview — not just their names, but what behaviors each one describes. Then prepare specific stories from your work experience that demonstrate each one. The STAR format (Situation, Task, Action, Result) is the standard structure Amazon expects.

A few principles come up more often than others for data science roles: Customer Obsession, Dive Deep, Bias for Action, Have Backbone; Disagree and Commit, and Earn Trust. Have at least one strong story for each of these, and make sure your stories have concrete, quantifiable outcomes.

The biggest mistake candidates make is being vague. "I worked with the team to improve the metric" isn't a story. "I identified that our signup funnel had a 40% drop-off at step three, ran an experiment on a simplified form, and reduced drop-off by 15% over two weeks" is a story. Amazon interviewers will follow up aggressively on specifics — if you can't go deep on your own example, they'll notice.

SQL and coding

Amazon leans harder on SQL than many of its peers. The SQL rounds involve realistic business scenarios with multiple tables, requiring joins, CTEs, subqueries, and window functions. The problems are roughly at an intermediate level in terms of complexity — the emphasis is on correctness, clean structure, and the ability to work through multi-step problems without getting lost.

Some teams also include a coding round with algorithm-style problems — typically at a LeetCode medium level, testing data structures, array manipulation, and basic algorithm implementation. Whether you get this round depends on the specific team and role level. Ask your recruiter what to expect.

Statistics and machine learning

Amazon's stats round covers hypothesis testing, confidence intervals, A/B test design, Bayesian reasoning, and practical significance versus statistical significance. The questions start straightforward and escalate — you might begin with a basic hypothesis test and end up discussing multiple comparison corrections or the tradeoffs of a particular experimental design.

The machine learning round (if included — it depends on the role) covers algorithm selection, model evaluation, feature engineering, and tradeoff discussions. Amazon cares about practical ML judgment: given this business problem and this data, what approach would you choose and why? What are the failure modes? How would you evaluate whether it's working?

What actually matters

Amazon's interview comes down to this: can you combine technical competence with clear, evidence-based decision-making and the ability to operate with ownership?

The Leadership Principles aren't a gimmick. They're how Amazon evaluates whether you'll actually function well inside the organization. A brilliant data scientist who can't articulate how they've demonstrated Customer Obsession or Dive Deep will lose to a solid data scientist who can.

Prep your stories with the same rigor you'd prep your SQL. Quantify your impact. Be specific about your individual contribution versus the team's. And practice telling each story in under three minutes — Amazon interviewers move fast, and a rambling answer is almost as bad as a weak one.

(Rabbit Hole — practice analytics cases and case studies tailored to the kind of business problems you'll face at Amazon.)