Amazon Data Scientist interview typically runs 3–4 rounds: online assessment, phone screen, and a 5–6 hour virtual loop with coding, ML depth/breadth, system design, and behavioral interviews. The process takes 2–4 weeks and is distinguished by Amazon Leadership Principles integrated throughout every round.
$122K
Avg. Base Comp
$230K
Avg. Total Comp
4-6
Typical Rounds
2-4 weeks
Process Length
What makes Amazon's data science process genuinely different from most tech companies is that the Leadership Principles aren't a separate behavioral layer — they're woven directly into technical conversations. Multiple candidates reported that interviewers would pivot mid-discussion from a question about bias-variance tradeoff or causal inference directly into a probing LP scenario, and the transition was seamless and intentional. One candidate noted that recognizing which specific principle the interviewer was probing — and orienting their answer accordingly — was the key skill that separated a good answer from a great one. That's a non-obvious nuance that most candidates miss entirely.
The technical breadth expectation is also wider than people anticipate. We've seen questions spanning 2D binary search, chamfer distance, KL divergence and its relationship to cross-entropy, transformer architecture deep dives, causal inference methods like difference-in-differences and synthetic control, and open-ended forecasting problems — sometimes across a single loop. The candidate who received an offer specifically emphasized that interviewers cared more about reasoning process than final answers, particularly in the science application rounds. The one candidate who described a genuinely disorganized experience — an interviewer who didn't know why the role was open or what team it was for — is the exception, not the rule, but it's a reminder that loop quality can vary meaningfully by team.
Project deep dives are where many candidates underestimate the stakes. Amazon interviewers consistently pushed on failure cases — one candidate was explicitly asked to describe a time they overfit a model — and on the specific decisions behind past work, not just the outcomes. Being able to articulate why you made a particular modeling choice, what tradeoffs you accepted, and what you'd do differently matters more here than at most companies we track.
Synthetized from 8 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Amazon process.
My interview process had one HR round, then a technical loop that felt like a mix of coding, ML depth, ML breadth, and project discussion. In my case there was also an online assessment before the live interviews, and after that I scheduled two interviews in early April. The first half hour was behavioral, then they spent a good amount of time digging into my research and past projects, and after that the conversation moved into deep learning topics. In the broader loop, there were four technical interviews total, including one round focused on going deep on my own work and another general data science round that also included system design. That system design portion was about modeling an end-to-end pipeline for translating one set of multimodal objects into another language, which was more open-ended than a standard coding interview. The coding round was LeetCode-style and tested algorithmic problem-solving, data structures, speed, and edge cases. One specific question I got was 2D binary search, and another coding problem was to compute the chamfer distance efficiently. The ML questions were a mix of breadth and depth, so I was asked about metrics like precision, recall, AUC-ROC, and perplexity, along with deeper questions on deep learning and how I would evaluate models. The science/application round was also fairly practical and asked me to design three evaluation metrics to select high-quality data from crowdsourced data. Overall, the difficulty was moderate to hard, mostly because it combined fast coding with fairly deep ML knowledge and project-specific follow-up, so it was not just one type of interview. I did not get an offer. My main takeaway is to be ready for both classic coding problems and detailed ML discussion, especially around evaluation metrics and your own research or project decisions. It also helps to practice explaining an end-to-end ML pipeline clearly, since the system design portion was very open-ended.
Prep tip from this candidate
Be ready for a LeetCode-style coding round that can include 2D binary search or an efficient geometric computation like chamfer distance, and don’t neglect edge cases and speed. Also prepare to defend your own research/projects in depth and to discuss ML evaluation metrics such as precision, recall, AUC-ROC, and perplexity, since those came up directly.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Comments Histogram | |
| Top Three Salaries | |
| Upsell Transactions | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Average Quantity | |
| Experiment Validity | |
| Manager Team Sizes | |
| Random SQL Sample | |
| Download Facts | |
| Flight Records | |
| Paired Products | |
| Prime to N | |
| Swipe Precision | |
| Monthly Customer Report | |
| Longest Streak Users | |
| Always Excited Users | |
| Recurring Character | |
| Exam Scores | |
| Compute Deviation | |
| Jars and Coins | |
| Cumulative Sales Since Last Restocking | |
| Retailer Data Warehouse | |
| Completed Shipments | |
| Permutation Palindrome |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with a recruiter or HR representative focused on your background, resume walkthrough, and general fit. Expect at least one behavioral question tied to Amazon's Leadership Principles and a discussion of your experience rather than deep technical screening.
A take-home assessment that typically includes SQL multiple-choice and coding questions, Python data science stack questions (numpy, pandas), algorithm problems, and Leadership Principles questions. Difficulty is moderate and tests foundational coding and data science skills.
A phone or video screen with the hiring manager or a team member that combines a resume and project deep-dive with ML basics, SQL and Python coding at medium difficulty, and behavioral questions in STAR format tied to specific Leadership Principles. Some candidates also receive a take-home project at this stage lasting approximately one week.
A multi-round virtual loop with back-to-back interviews typically covering LeetCode-style coding (easy to hard, e.g., 2D binary search, chamfer distance, string manipulation), ML breadth and depth (metrics, bias-variance tradeoff, transformer architecture, KL divergence, regularization), a project and research deep-dive probing past decisions and failure cases, a science application round with practical problems (e.g., designing evaluation metrics, forecasting Titanic views), and a system design or causal inference round (e.g., end-to-end ML pipelines, difference-in-differences, synthetic control). Some loops also include a tech talk or presentation of past work.
A dedicated bar raiser interview that is especially rigorous, often including in-depth code review, Python coding on the fly, SQL aggregation questions, and behavioral questions tied to Leadership Principles such as describing a time you overfit a model or handled a conflict with leadership.
After the loop concludes, Amazon typically delivers a hiring decision within approximately 5 business days.