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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 91–120 of 1971 results
- AmazonMediumData ScientistDescribe Overcoming Obstacles and Taking Calculated Risks
Evaluates Amazon behavioral interview readiness for calculated risk-taking and anticipated obstacles. Strong answers use STAR, explain risk calculus, data, mitigations, guardrails, and contingency plans, then quantify the outcome and reflect on what changed afterward.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDescribe Your Most Challenging Project and Its Outcome
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Challenging Project and Its Outcome states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonEasyData ScientistDescribe a challenging project
This question evaluates a data scientist's Deliver Results competency, focusing on ownership, decision-making under ambiguity, trade-off analysis, stakeholder management, and the ability to deliver measurable business impact.
Behavioral & LeadershipTechnical Screen - AmazonEasyData ScientistDescribe a time you solved a complex problem
This question evaluates analytical problem-solving, investigative data-analysis, data validation, and leadership/ownership competencies in the Behavioral & Leadership domain for a Data Scientist role.
Behavioral & LeadershipTechnical Screen - AmazonHardData ScientistDescribe missed deadline and scope expansion
This question evaluates accountability, ownership, project planning, stakeholder communication, risk-tradeoff analysis, and impact measurement competencies for a data scientist.
Behavioral & LeadershipHR Screen - AmazonHardData ScientistDesign A/B Test for New Amazon Recommendation Module
Design A/B Test for New Amazon Recommendation Module evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistDesign Incremental Load Process for Large Relational Table
This question evaluates understanding of incremental loading, change-data-capture, idempotent upsert logic, deduplication, partitioning, and data quality controls for large relational tables within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumData ScientistDesign a Churn Model: Handle Missing Data and Justify
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design a Churn Model: Handle Missing Data and Justify states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonHardData ScientistDesign a Machine Learning Recommendation System Pipeline
Design a Machine Learning Recommendation System Pipeline evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonMediumData ScientistDesign a Scalable, Fault-Tolerant Distributed Order System
This question evaluates system-design and algorithmic competencies, combining distributed-systems concepts — high availability, low-latency architecture, scalability levers, and fault-tolerance mechanisms — with practical binary-tree traversal and time–space complexity analysis.
Coding & AlgorithmsOnsite - AmazonHardData ScientistDesign a robust traffic forecasting pipeline
This question tests the ability to design an end-to-end, scalable time-series forecasting pipeline, focusing on anomaly handling, structural decomposition, uncertainty quantification, and model validation strategies.
Machine LearningTechnical ScreenTime-SeriesForecastingState-Space Models - AmazonMediumData ScientistDesign an A/B Test for Dashboard Engagement Impact
Amazon data scientist experimentation prompt on designing an A/B test for an energy-usage dashboard redesign, including hypotheses, metrics, guardrails, power, test duration, seasonality, and analysis methods.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistDesign an Automated Home-Price Valuation Model
Design an Automated Home-Price Valuation Model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonHardData ScientistDesign an ML Model for Interview Recommendation Pipeline
Design an ML Model for Interview Recommendation Pipeline evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonHardData ScientistDesign an end-to-end spam detection system
This question evaluates a data scientist's system-design and applied machine learning engineering skills—covering problem framing and labeling, feature representation, model selection and calibration, real-time serving constraints, drift detection, and feedback/safety mechanisms—and is commonly asked to probe trade-offs between latency, precision/recall, and robustness against adversarial evolution in production spam detection. Category: Machine Learning; it tests machine learning systems and production-ML competencies at both conceptual-design and practical-application levels, emphasizing calibration, evaluation (offline and online), operational reliability, and rollback/mitigation planning.
Machine LearningTechnical Screen - AmazonHardData ScientistDesign an operations dashboard with justifications
This question evaluates a data scientist's ability to define precise operational metrics, justify visualization choices, design interactive drill-downs and alerting, and architect aggregation strategies for high-volume same-day delivery events.
Analytics & ExperimentationOnsite - AmazonHardData ScientistDesign and analyze pricing-page A/B test
This question evaluates data-science competencies in online experiment design and analysis, including statistical power and sample-size estimation, variance-reduction methods, guardrail and SRM monitoring, randomization and contamination handling, sequential monitoring and stopping rules, heterogeneity/subgroup analysis, and result communication within the analytics & experimentation domain. It is commonly asked to assess practical application of statistical inference and experimental rigor in product and business contexts, primarily testing practical application while requiring conceptual understanding of underlying statistical principles and operational trade-offs.
Analytics & ExperimentationOnsite - AmazonEasyData ScientistDesign and evaluate a RAG system
This question evaluates a candidate's competency in designing and evaluating retrieval-augmented generation (RAG) systems, including document ingestion, chunking, embedding and retrieval strategies, reranking, prompt construction, grounding/citation, operational constraints like latency and freshness, permission handling, and evaluation metrics and failure modes. It is commonly asked to assess practical system-design and applied machine learning skills for LLM applications, testing knowledge in the Machine Learning/Information Retrieval domain and requiring both practical application-level reasoning about trade-offs (latency, cost, precision vs. recall) and conceptual understanding of evaluation and guardrails.
Machine LearningTechnical Screen - AmazonHardData ScientistDesign causal study for reminder impact
This question evaluates a data scientist's competency in observational causal inference and program evaluation, covering concepts such as staggered rollouts, treatment definition, difference-in-differences diagnostics, matching/weighting, negative controls, interference handling, and power/MDE planning.
Analytics & ExperimentationHR Screen - AmazonHardData ScientistDesign end-to-end regression for energy demand
This question evaluates a candidate's ability to design and justify an end-to-end regression system for next-day site-level energy demand forecasting, testing competencies in time-series modeling, feature engineering, regularization, evaluation metrics and SLAs, production ML pipelines, monitoring, explainability, and data privacy.
Machine LearningOnsite - AmazonHardData ScientistDesign fraud detection across channels with unknowns
This question tests the ability to design an end-to-end fraud detection system that accounts for cold-start problems, cross-channel signals, and the trade-off between customer friction and loss mitigation.
Machine LearningOnsiteFraud DetectionAnomaly DetectionGraph Neural Networks - AmazonMediumData ScientistDesign student–course data models and SQL
This question evaluates data modeling, database design, and SQL proficiency—covering normalization, schema constraints, indexing, star-schema warehousing, slowly changing dimensions, partitioning strategies, and complex analytic queries—in the domain of relational databases and data warehousing (Data Manipulation/SQL).
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistDetermine Discount's Effect on Conversion Rate with A/B Testing
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Discount's Effect on Conversion Rate with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - AmazonMediumData ScientistDetermine Length of Longest Unique Substring
This question evaluates string-processing skills and algorithmic problem-solving, including handling character uniqueness and performance considerations. Commonly asked in Coding & Algorithms interviews to assess a candidate's ability to implement efficient solutions and reason about time/space trade-offs, it targets practical application rather than purely conceptual understanding.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistDetermine Probability of Both Children Being Boys
Evaluates conditional probability reasoning in the classic two-children problem with different observation mechanisms. Strong answers enumerate ordered outcomes, condition correctly, and compare at-least-one, older-child, and random-child cases.
Statistics & MathTechnical Screen - AmazonMediumData ScientistDiagnose Bias–Variance Trade-off in Supervised Learning
Diagnose Bias–Variance Trade-off in Supervised Learning evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonMediumData ScientistDiagnose Business Decline Using Key Data Metrics
Diagnose Business Decline Using Key Data Metrics evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistDiagnose Causes and Test Hypotheses for Metric Drop
Evaluates incident-style analysis of a sudden purchase-conversion drop in a consumer product. Strong answers list plausible causes, validate with concrete cuts and logs, design an A/B test, and define metrics.
Analytics & ExperimentationTechnical Screen - AmazonMediumData ScientistDiagnose MySQL joins and GROUP BY/HAVING errors
This question evaluates mastery of SQL join semantics (LEFT, RIGHT, FULL OUTER emulation, CROSS), NULL equality and duplicate handling, GROUP BY/HAVING behavior under ONLY_FULL_GROUP_BY, aggregation result shaping and type/format conversions in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Online Assessment - AmazonHardData ScientistDrive stakeholder alignment under trade-offs
This question evaluates competency in cross-functional stakeholder alignment, trade-off analysis, data governance and security risk assessment, executive communication, and program decision-making within a technical product context.
Behavioral & LeadershipOnline Assessment
Amazon