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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
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- 11
- Topics
- 8
- Languages
Showing 31–60 of 641 results
- 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 - 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 - 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 - 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 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 - 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 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 - AmazonHardData ScientistEstimate Treatment Effects Using PSM, DiD, and DML Methods
Estimate Treatment Effects Using PSM, DiD, and DML Methods evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - AmazonHardData ScientistEstimate live sports impact on subscriptions
This question evaluates a data scientist's competence in causal inference and observational study design within analytics and experimentation, covering skills such as defining treatment and control, selecting outcome metrics and covariates, and choosing identification strategies like matching, IV, and difference-in-differences; it is commonly asked to assess the ability to produce credible causal estimates when randomized experiments are unavailable. It tests both conceptual understanding of identification assumptions (e.g., unconfoundedness, overlap, SUTVA, IV validity) and practical application of matching algorithms, propensity modeling, regression specifications, diagnostics, and staggered-rollout/event-study designs for real-world program evaluation.
Analytics & ExperimentationOnsite - AmazonEasyData ScientistEvaluate NLP Classification Models
This question evaluates competency in NLP classification model evaluation, covering understanding of confusion matrices, precision/recall/F1/AUC metrics, thresholding and trade-offs, handling class imbalance, and connecting metric choices to operational actions and business costs.
Machine LearningOnsite - AmazonMediumData ScientistEvaluate Soft Skills Through Behavioral Interview Questions
Evaluates behavioral soft skills through data-science leadership prompts about helping teammates, diving deep, deadlines, independent decisions, and growth. Strong answers use STAR with concrete actions and outcomes.
Behavioral & LeadershipOnsite - AmazonHardData ScientistEvaluate concession gift-card policy with DID
This question evaluates a data scientist's competency in causal inference and experimentation design, encompassing difference-in-differences with staggered adoption and event-study diagnostics, data inventory and unit-of-analysis specification, clustering choices, heterogeneity and guardrail analyses, and cost–benefit and optional randomized experiment design. Commonly asked in Analytics & Experimentation interviews because it gauges the ability to justify identification strategies, diagnose violations of parallel trends and propose alternatives, and translate empirical results into business-impact metrics, it sits at the intersection of conceptual understanding and practical application within the Data Science domain.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistExplore Dataset to Assess Quality and Choose Visualizations
Evaluates fast dataset exploration, data-quality assessment, and visualization planning for business stakeholders. Strong answers profile schema, check missingness and bias, and choose charts aligned to decisions.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistFind Top-3 Salaries Per Department Using SQL
This question evaluates SQL data manipulation competency, focusing on group-wise aggregation, ordering, and selecting the top-N salary values within each department.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistFind top-spend categories per customer with ranking
This question evaluates proficiency with SQL aggregation and JOINs, analytic/window functions (RANK()), tie and NULL/edge-case handling, and percent-of-total calculations for per-customer spend analysis.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistIdentify First Daily Order for Each Merchant
This question evaluates proficiency in data manipulation with SQL and related tools, focusing on identifying the first transaction per merchant for each calendar date and handling ordering and deduplication within grouped transactional records.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistIdentify P-Value Limitations and Complementary Approaches
Identify P-Value Limitations and Complementary Approaches evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - AmazonMediumData ScientistImplement Reservoir Sampling; Analyze Time and Space Complexity
This question evaluates understanding of streaming randomized algorithms and probabilistic reasoning—specifically reservoir sampling—along with analysis of time and space complexity and use of hash-based structures for duplicate detection; it is in the Coding & Algorithms domain and tests both conceptual probability invariants and practical implementation skills. Such problems are commonly asked to gauge a candidate's ability to design one-pass, linear-time solutions for unbounded data streams and to reason about algorithmic correctness and resource bounds in technical interviews.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistImplement and Analyze 'Merge k Sorted Lists' Algorithm
This question evaluates algorithm design skills and complexity analysis for merging multiple sorted sequences, measuring proficiency with appropriate data structures, time-space trade-offs, and correctness reasoning.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistImplement list overlap and dense-ranked word frequencies
This question evaluates a data scientist's ability to implement efficient multiset intersection and streaming top-k word-frequency computations, covering duplicate-aware counting, dense-ranking, Unicode handling, and strict time/space constraints.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistImplement streaming k-way merge with constraints
This question evaluates competence in streaming algorithms and resource-constrained algorithm design, specifically implementing a k-way merge over blocking, potentially unbounded integer iterators while preserving stable duplicates and reasoning about time and O(k) space complexity.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistNavigate Crises and Sustain Team Morale Under Pressure
Navigate Crises and Sustain Team Morale Under Pressure evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistOptimize Email Strategy for New Prime Video Series Launch
Evaluates email targeting and ranking strategy for a new Prime Video series launch. Strong answers cover data needs, cold start, ranking models, offline metrics, A/B testing, guardrails, and model selection.
Machine LearningOnsite - AmazonMediumData ScientistOptimize XGBoost for Predicting Marketing Outcomes
Optimize XGBoost for Predicting Marketing Outcomes 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 ScientistResolve Conflicts Between Data Findings and Team Opinions
Resolve Conflicts Between Data Findings and Team Opinions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite
Amazon