Data Scientist Interview Questions

Showing 1–30 of 281 results

• AffirmEasy·Data Scientist
  Calculate Profit of 4-Month Loan at 30% APR

  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 Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AffirmMedium·Data Scientist
  Diagnose Sudden Drop in Credit-Card Approval Rate

  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 Diagnose Sudden Drop in Credit-Card Approval Rate states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Define Success Metrics and Experiment Plan for Product Development

  Evaluates product-planning metrics and experiment design for a two-sided marketplace booking funnel. Strong answers define a primary booking metric, diagnostic and guardrail metrics, randomization unit, sample size, marketplace spillovers, and launch criteria before development.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Design a network-aware Wi‑Fi badge experiment

  This question evaluates a data scientist's proficiency in experimental design, causal inference under interference (including SUTVA risks), metric specification, power and sample‑size computation, analysis planning, and translating treatment effects into financial decision rules.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Design an A/B test with causal inference

  This question evaluates skills in experimental design, causal inference, and applied statistics — including estimand selection, sample-size calculation under clustering, integrity monitoring, handling noncompliance and contamination, sequential monitoring, and two-proportion inference — within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked because interviewers need to assess the ability to design and analyze robust A/B tests under real-world constraints; the prompt requires both conceptual understanding of causal assumptions and practical application of power calculations, diagnostics, and monitoring procedures.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Design and Analyze Airbnb Locker Experiment

  This question evaluates a candidate's skills in experimental design and causal inference for marketplace features, including selecting the appropriate randomization unit, defining eligibility and treatment/control arms, and choosing primary, secondary, and guardrail metrics.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Design robust primary and guardrail metrics

  This question evaluates proficiency in experiment metric design, guardrail definition, data quality diagnostics, and statistical analysis for A/B testing, including power calculations and sequential monitoring, within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Estimate Causal Impact Using Synthetic Control Methods

  Evaluates causal impact estimation after a full product rollout with no holdout. Strong answers use interrupted time series, synthetic control, Bayesian structural time series, exposure variation, and placebo tests to build and validate a credible counterfactual.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Estimate impact of global launch without holdout

  This question evaluates a candidate's competence in causal inference, observational study design, and statistical uncertainty quantification for product analytics, specifically estimating lift after a global launch without a holdout while addressing confounding, interference, and operational data-quality issues.

  Analytics & ExperimentationTechnical Screen
• AirtableEasy·Data Scientist
  Diagnose metric anomalies and evaluate new algorithm

  This question tests your ability to systematically investigate a sudden drop in a key product metric and design a robust A/B test to evaluate the impact of a new recommendation algorithm.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct AnalyticsRoot Cause Analysis
• AirwallexMedium·Data Scientist
  Diagnose a Structural Break in KYC Submission Rate

  Tests your ability to systematically diagnose sudden metric shifts, secular downward trends, and seasonality in a fintech onboarding funnel using structured hypothesis testing and slice analysis.

  Analytics & ExperimentationTechnical Screenmetric-diagnosisfunnel-analysismix-shift
• AmazonMedium·Data Scientist
  Determine 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
• AmazonMedium·Data Scientist
  Diagnose 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
• AmazonMedium·Data Scientist
  Estimate shuttle impact with robust causal design

  This question evaluates a candidate's competency in causal inference and experimental analytics, covering staggered-adoption difference-in-differences design, estimand and outcome definition, parallel-trends diagnostics, selection and time-varying confounding considerations, clustering and weighting choices, handling varied adoption timing and site attrition, robustness checks, and communication of coefficient interpretation and uncertainty. It is commonly asked in the Analytics & Experimentation domain because it tests both conceptual understanding of identification assumptions and practical application of statistical design choices for real-world observational causal analysis.

  Analytics & ExperimentationTechnical Screen
• AmazonEasy·Data Scientist
  How to evaluate adding video ads in a game

  This question tests your ability to design an experimentation framework and evaluate the business impact of introducing interstitial video ads in a mobile game, focusing on the trade-off between short-term revenue and long-term user retention.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct AnalyticsMonetization
• AmazonMedium·Data Scientist
  How would you evaluate adding video ads?

  This question evaluates a data scientist's competencies in experimental design, causal inference, metrics selection, and product monetization analytics within the Analytics & Experimentation domain, focusing on measuring the impact of video ads on revenue, engagement, retention, and lifetime value.

  Analytics & ExperimentationTechnical Screen
• AmazonMedium·Data Scientist
  How would you test a price increase?

  An Amazon data scientist technical screen case on pricing: decide whether to raise the price of a subscription product, choose the price point and packaging, and design the experiment that proves it. The answer covers per-visitor profit and LTV metrics, guardrails, elasticity estimation, randomization on the billing account, and how to handle existing subscribers at renewal. It also fixes the two traps that sink most answers: judging a subscription price test on short-horizon revenue, and measuring churn only among users who converted.

  Analytics & ExperimentationTechnical Screen
• AmazonHard·Data Scientist
  Prioritize a new warehouse proposal with data

  This question evaluates demand forecasting, network optimization, five-year NPV financial modeling, experiment and quasi-experiment design for incrementality estimation, sensitivity analysis, and stakeholder communication within the analytics & experimentation domain for data science roles focused on warehouse/fulfillment center decisions.

  Analytics & ExperimentationTechnical Screen
• AmazonMedium·Data Scientist
  Profile and visualize an unfamiliar dataset

  This question evaluates a data scientist's competency in exploratory data analysis, data quality assessment, time-series aggregation, visualization design, and concise stakeholder communication when working with an undocumented events-plus-purchases CSV.

  Analytics & ExperimentationTechnical Screen
• AmazonEasy·Data Scientist
  Walk through an A/B test end-to-end

  This question evaluates a data scientist's competencies in experimental design, statistical inference, causal reasoning, metric selection, and data quality assurance for A/B testing.

  Analytics & ExperimentationTechnical Screen
• AppleMedium·Data Scientist
  Design A/B Test for Search Feature Effectiveness

  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 Search Feature Effectiveness states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AppleMedium·Data Scientist
  Diagnose post-release conversion regression rigorously

  This question tests the ability to diagnose a post-release metric regression in the absence of a control group by utilizing quasi-experimental design and causal inference methods.

  Analytics & ExperimentationTechnical ScreenCausal InferenceRoot Cause AnalysisExperimentation
• AppleMedium·Data Scientist
  Examine Data to Boost Instagram Purchases Effectively

  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 Examine Data to Boost Instagram Purchases Effectively states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• Bank of AmericaMedium·Data Scientist
  Discuss a Recent Financial-Market Trend

  Tests your ability to formulate and rigorously analyze a recent financial-market trend by decomposing asset value changes versus net organic investor flows, designing econometric hypothesis tests, and evaluating data limitations.

  Analytics & ExperimentationTechnical ScreenFinancial AnalyticsTime SeriesHypothesis Testing
• BBetterHelpHard·Data Scientist
  Optimize Experiment Thresholds for Impactful Feature Launches

  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 Optimize Experiment Thresholds for Impactful Feature Launches states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• Boston Consulting GroupMedium·Data Scientist
  Identify Causes and Solutions for Fashion Profit Decline

  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 Identify Causes and Solutions for Fashion Profit Decline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• BByteDanceMedium·Data Scientist
  Diagnose Low CTR in an Advertising Campaign Funnel

  Prepare for a product analytics interview by diagnosing below-average advertising CTR from limited campaign-level funnel metrics. The question tests metric interpretation, uncertainty, evidence-versus-hypothesis discipline, and how to plan useful follow-up analysis.

  Analytics & ExperimentationTechnical Screen
• CVS HealthHard·Data Scientist
  Design a flu-shot A/B/n campaign experiment

  This question evaluates a data scientist's competency in experimental design, causal inference, sample-size and power calculations, compliance-adjusted effect estimation, attribution instrumentation, and reporting for multi-channel marketing experiments.

  Analytics & ExperimentationTechnical Screen
• CVS HealthHard·Data Scientist
  Launch and measure a TV campaign

  This question evaluates a data scientist's competency in experimental design and causal inference, including geo-level matching, KPI definition, media measurement (GRP/TRP, reach/frequency, adstock and saturation modeling), power analysis, and operational risk management for measuring incremental vaccinations.

  Analytics & ExperimentationTechnical Screen
• Capital OneMedium·Data Scientist
  Analyze ad watch-time with Excel pivots

  This question evaluates a data scientist's proficiency with Excel PivotTables, date and ISO-week handling, aggregation and ratio metrics (clicks per watch-minute), and deterministic tie-breaking logic for reproducible results.

  Analytics & ExperimentationTechnical Screen