Amazon Data Scientist candidates report assessments and screens that combine SQL, Python or coding, ML and statistics, project discussion, business cases, and Leadership Principles.
$169K
Avg. Base Comp
$260K
Avg. Total Comp
2-5 rounds
Typical Rounds
2-5 weeks
Process Length
Amazon Data Scientist interviews in the supplied reports can include an online assessment, a technical conversation, and a longer virtual loop, although the sequence and depth vary. Candidates describe a broad combination of SQL, algorithmic coding, statistics, machine learning, project discussion, business judgment, and Amazon Leadership Principles.
For technical preparation, practice explaining decisions rather than giving only a final answer. Reported topics include hypothesis testing, supervised versus unsupervised learning, data reshaping, model-evaluation metrics, feature engineering, biased data, and end-to-end ML pipelines. Coding has included tree and graph problems, two-dimensional binary search, and other algorithmic exercises. Some roles also probe causal inference through business-impact cases, including difference-in-differences, synthetic control, and instrumental variables.
Leadership Principles may appear throughout the process rather than in a single behavioral round. Candidates were asked about solving problems with data, working through ambiguity, facing resistance, disagreeing with leadership, and handling stress. Prepare concise STAR examples that make your individual actions and results clear, then expect follow-up questions about why you chose a particular approach.
Some candidates also encountered applied cases involving A/B-test design, predictive modeling, forecasting, or selecting quality metrics. State the business goal, assumptions, required data, evaluation plan, and limitations before recommending a method. Ask the recruiter which technical areas and interview formats apply to the specific team.
Synthesized from 38 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The most memorable part of my Amazon Data Scientist loop was that every interviewer tied their questions back to Leadership Principles, even when the conversation was technical. The interviewer usually began with a high-level overview of the role and then moved into the main discussion, which could range from research-related topics to testing ML depth and breadth. I was also asked to describe a time when I set an initial goal and ultimately delivered a result that exceeded it, so I made sure to give a concrete example with a clear impact.
The technical portion covered both broad ML knowledge and deeper reasoning about models and research choices. I was expected to explain my thinking rather than just name concepts. There was also product-oriented system design: I was asked how I would design an agentic recommendation system, including the architecture, models or components I would choose, and how I would evaluate both quality and safety. That was a useful reminder that the role was not limited to modeling in isolation; I needed to connect ML decisions to an end product.
Across the process, the interviewers left time for my questions at the end. My biggest takeaway is to prepare Leadership Principle stories as carefully as the technical material, and to practice explaining ML systems from problem framing through evaluation and safety considerations. I received and accepted an offer.
Prep tip from this candidate
Prepare Leadership Principle examples for every round, including a story about exceeding an initial goal. Practice designing an agentic recommendation system end to end, with explicit choices for components, evaluation, and safety.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an online assessment in some processes, with SQL, coding, probability or statistics, and Leadership Principles or work-simulation content. Other candidates describe a brief recruiter conversation first. Assessment coding has ranged from SQL tasks to algorithmic questions, including tree or graph-style problems.
A technical screen may combine a walkthrough of prior ML work with SQL, Python, or coding. Candidates report follow-ups on model selection, evaluation metrics, project decisions, and business impact, alongside topics such as PCA, regularization, or core machine-learning concepts.
Candidates report multiple virtual interviews covering project depth, data manipulation, ML breadth, behavioral questions, and sometimes coding. Python data work has included pandas operations; other reports describe SQL questions or algorithmic coding. Leadership Principle prompts may appear within technical conversations.
Some candidates report a case tied to the hiring team or a business-impact question. Examples include A/B-test design, handling biased data, out-of-stock customer behavior, and estimating causal impact without an experiment. Candidates should clarify the problem, make assumptions explicit, and explain their analytical choice.
Candidates sometimes report a Bar Raiser or hiring-manager conversation focused heavily on Leadership Principles, situational judgment, and detailed examples from data work. The technical balance varies: one candidate reported a leadership-only final loop, while others described technical and behavioral questions together.
