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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.
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Showing 1–30 of 201 results
- AdobeMediumData ScientistDesign Metrics Framework for Adobe Express Performance Evaluation
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 Design Metrics Framework for Adobe Express Performance Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AirbnbHardData ScientistDesign and assess an A/B test
This problem tests your ability to design a statistically sound A/B test for a marketplace platform, including metric selection, power analysis considerations, and handling potential interference effects.
Analytics & ExperimentationOnsiteA/B TestingExperimental DesignMetrics - AirwallexMediumData ScientistDiagnose a dip in approval/conversion rate
This Analytics & Experimentation prompt evaluates time-series diagnostics, ratio decomposition (numerator/denominator and traffic mix), segmentation and data-quality triage, and causal inference skills at a staff-level applied analytics abstraction.
Analytics & ExperimentationOnsite - AirwallexEasyData ScientistDiagnose conversion-rate time series and CTA swap
This question evaluates a data scientist's competency in time-series diagnosis, conversion-rate decomposition, causal inference, and A/B experiment design within the Analytics & Experimentation category of Data Science.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistHow would you measure billboard impact?
This problem tests your ability to design a quasi-experimental framework for measuring the offline impact of an OOH (Out-of-Home) advertising campaign on digital conversion metrics.
Analytics & ExperimentationOnsiteCausal InferenceMetricsExperimental Design - AirwallexMediumData ScientistHow would you test swapping two CTA buttons?
This question evaluates A/B test design, causal inference, funnel metric selection and diagnostic/guardrail thinking within Analytics & Experimentation for a Data Scientist role, and is commonly asked to gauge the ability to reason about user behavior, conversion trade-offs, and risk management in sensitive domains like fintech.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistInvestigate Harassment Surge and Mitigation
An Airwallex Data Scientist onsite analytics question: a monthly moderation report shows the Harassment violation type spiking, and you must decide whether the surge is real or a measurement artifact, then propose and test mitigations. It probes diagnostic analytics, prevalence-metric design, denominator effects, Simpson's paradox, selection bias, label drift, model calibration, causal inference (ITS/DiD), and experiment design with guardrails.
Analytics & ExperimentationOnsite - AmazonHardData ScientistAnalyze an A/B test over last 7 days
This question evaluates competency in A/B test analysis and experiment design, covering conversion-rate computation, statistical inference (two-proportion testing and confidence intervals), sample-ratio checks, heterogeneity assessment (fixed versus random effects), and power/MDE reasoning.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistChoose Effective Graphs for Data Exploration
Evaluates chart selection for exploratory data analysis across categorical and numerical variables. Strong answers explain pie charts, distributions, comparisons, relationships, missingness, and when visuals mislead.
Analytics & ExperimentationOnsite - AmazonHardData ScientistCompare Tableau live vs extract and filters
This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.
Analytics & ExperimentationOnsite - 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 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 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 - 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 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 - 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 - AppleHardData ScientistChoose Optimal Network Retry Threshold
This question evaluates competence in statistical analysis, experimental design, and decision-making under trade-offs between playback reliability and battery or radio power consumption.
Analytics & ExperimentationOnsite - AppleMediumData ScientistDesign an A/B Test for Homepage Layout Impact
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 Design an A/B Test for Homepage Layout Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AppleHardData ScientistEvaluate a model and choose metrics
This question evaluates a data scientist's competency in cost-sensitive model evaluation, handling extreme class imbalance, calibration and threshold derivation, experiment design, and post-launch monitoring and fairness within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - AtlassianMediumData ScientistDiagnose Checkout Rate Drop: Steps and Analyses
Diagnose Checkout Rate Drop: Steps and Analyses 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 - Capital OneMediumData ScientistAnalyze your favorite app and improve it
This question evaluates a data scientist's competencies in product analytics, monetization analysis, competitive positioning, metric specification (north-star and guardrails), and experimental design including power and validity considerations.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistAssess Customer Value with Varied Contract Terms and Costs
Evaluates subscription contract unit economics across free periods, service costs, marketing costs, churn penalties, and fixed overhead. Strong answers compute customer value, portfolio value, and break-even acquisitions.
Analytics & ExperimentationOnsite - Capital OneHardData ScientistBuild a causal ML pipeline end-to-end
This question evaluates expertise in causal inference and policy learning, covering ATE/CATE estimation, heterogeneous treatment effect methods, off‑policy evaluation, overlap and sensitivity diagnostics, and operational deployment with fairness guardrails in the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - Capital OneHardData ScientistDefine and validate an airline profitability metric
This question evaluates competency in metric design, business analytics, causal inference, and experimental design by requiring a decomposable airline route profitability metric, operational guardrails, validation via backtests and sensitivity analyses, and a policy experiment plan.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistDesign A/B Test for Marketing Campaign Impact Evaluation
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 Design A/B Test for Marketing Campaign Impact Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistDesign a better water bottle and test it
This question evaluates experimental design, product analytics, hypothesis formulation, metric selection, sample size/power/MDE estimation, segmentation, and risk and rollback planning within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistDesign fundraising experiment and guardrails
This question evaluates experimental design, statistical power and sample-size calculation, selection of primary and guardrail metrics, segmentation with multiple-testing control or hierarchical modeling, stopping rules for sequential analysis, and revenue-impact calculations for A/B testing of email solicitations.
Analytics & ExperimentationOnsite - Capital OneEasyData ScientistDiagnose Flight Delays and Burger Launch
This question evaluates your ability to diagnose statistical modeling trade-offs on noisy operational flight data and perform a structured financial break-even analysis for a new lounge product launch.
Analytics & ExperimentationOnsiteregressioncausal-inferencebreak-even-analysis
Adobe
Airbnb
Airwallex
Amazon
Apple
Atlassian
Capital One