Data Scientist Interview Questions

Showing 1–30 of 88 results

• AirbnbMedium·Data Scientist
  Describe How You Use SQL in Data Science Work

  This question assesses a candidate's ability to articulate their technical workflow in SQL, focusing on data extraction strategies, quality assurance, and how technical analysis translates into business impact.

  Data Manipulation (SQL/Python)HR ScreenSQLData ScienceAnalytics
• AirbnbMedium·Data Scientist
  Present and Defend a Product Experiment You Designed

  Tests a candidate's ability to communicate and defend an end-to-end product A/B test with technical depth, covering hypothesis formulation, experimental design, metric trade-offs, and post-experiment business decisions.

  Behavioral & LeadershipHR ScreenA/B TestingExperimentationBehavioral
• AirbnbMedium·Data Scientist
  Walk Through an Experiment From Design to Decision

  Walk through an end-to-end A/B test for an online marketplace feature, covering metric definition, randomization design, SRM diagnosis, and making a launch decision with conflicting trade-offs.

  Analytics & ExperimentationHR ScreenExperimentationA/B TestingProduct Analytics
• AmazonMedium·Data Scientist
  Build DID panel and compute effects in SQL

  This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

  Data Manipulation (SQL/Python)HR Screen
• AmazonHard·Data Scientist
  Describe 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
• AmazonHard·Data Scientist
  Design 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
• AmazonHard·Data Scientist
  Validate DID and IV assumptions rigorously

  This question evaluates mastery of causal inference and applied econometrics, specifically difference-in-differences, two-way fixed effects, staggered adoption and treatment heterogeneity, instrumental variables (2SLS), clustering, and GMM-based inference.

  Statistics & MathHR Screen
• AnthropicMedium·Data Scientist
  Describe a New Measurement That Changed a Business Decision

  Prepare a behavioral answer about creating a business metric, validating its definition, and demonstrating decision impact.

  Behavioral & LeadershipHR Screen
• AnthropicMedium·Data Scientist
  Tell Me About a Metric You Built That Changed a Real Decision

  A behavioral prompt asking the candidate to walk through a time they invented or redefined a metric, prove it was measuring the right thing, and show it actually changed what the business did next.

  Behavioral & LeadershipHR Screenbehavioralmetricscommunication
• AppleMedium·Data Scientist
  Clarify status, education, and multi-role strategy

  This question evaluates a candidate's ability to document and communicate U.S. work authorization details, resolve resume inconsistencies, articulate multi-level role positioning, and demonstrate minicase availability and preparation—assessing attention to detail, clarity of communication, stakeholder coordination, and practical readiness for data-science interviews. Commonly asked in Behavioral & Leadership screens for Data Scientist roles to verify eligibility, alignment with role expectations, and logistical readiness, it tests both conceptual understanding of career-leveling and contingency planning and practical application of documentation, messaging, and time-management.

  Behavioral & LeadershipHR Screen
• Capital OneEasy·Data Scientist
  Calculate Annual Profit of Credit Card Portfolio

  Evaluates annual profit calculation for a credit-card portfolio with monthly and annual revenue and cost components. Strong answers annualize figures, compute per-card profit, and scale to portfolio profit.

  Statistics & MathHR Screen
• Capital OneEasy·Data Scientist
  Calculate Incremental Customers for Marketing Spend Justification

  Evaluates incremental customer calculations for partnership marketing spend justification. Strong answers derive break-even customers for fixed spend and required volume for variable-cost campaigns against a profit target.

  Statistics & MathHR Screen
• Capital OneEasy·Data Scientist
  Calculate Profitability and Break-Even for Lyft Partnership Campaign

  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 Profitability and Break-Even for Lyft Partnership Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationHR Screen
• Capital OneMedium·Data Scientist
  Compute capacity, staffing trade-offs, and break-even

  This question tests the ability to model operational costs, evaluate staffing trade-offs between overtime and contracting, and perform break-even analysis under varying volume assumptions.

  Analytics & ExperimentationHR ScreenCapacity PlanningCost AnalysisOperations
• Capital OneMedium·Data Scientist
  Consider Key Factors Before Launching New Credit Card

  Evaluates launch-readiness analysis for a new consumer credit card across market, customer, risk, compliance, operations, and financial viability. Strong answers validate assumptions and define go/no-go criteria.

  Behavioral & LeadershipHR Screen
• Capital OneMedium·Data Scientist
  Decide content volume and price under uncertainty

  This question evaluates a candidate's competency in pricing and content-production optimization under uncertainty, including defining optimization objectives and constraints, specifying necessary metrics, and assessing sensitivity to changes in variable costs.

  Analytics & ExperimentationHR Screen
• Capital OneEasy·Data Scientist
  Differentiate fixed and variable costs with examples

  This question evaluates understanding of cost classification and unit economics—specifically the distinction between fixed and variable costs—and the competency to apply those concepts within a subscription streaming business context.

  Statistics & MathHR Screen
• Capital OneMedium·Data Scientist
  Explain MSE vs MAE, AUC, and imbalance handling

  This question evaluates a candidate's understanding of regression loss trade-offs (MAE vs MSE), classification evaluation metrics under severe class imbalance (ROC-AUC vs PR-AUC), neural network output activation and loss dynamics, and practical techniques for imbalance mitigation and probability-threshold selection in binary classification.

  Machine LearningHR Screen
• Capital OneEasy·Data Scientist
  Graph WTP vs content and explain cap

  This question evaluates understanding of piecewise linear functions, marginal analysis, and the economic interpretation of willingness-to-pay, within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathHR Screen
• Capital OneMedium·Data Scientist
  Impute missing values without leakage

  This question evaluates proficiency in data imputation, strict train/validation leakage prevention, temporal per-user propagation rules, and categorical aggregation for model-ready features.

  Data Manipulation (SQL/Python)HR Screen
• Capital OneMedium·Data Scientist
  Merge seven tables into one clean DataFrame

  This question evaluates proficiency with pandas-based data manipulation, including multi-table joins, aggregations, dtype enforcement, denormalization, and verification of row/column integrity within the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Data Manipulation (SQL/Python)HR Screen
• Capital OneMedium·Data Scientist
  Present Analytical Process and Recommendations for Credit-Card Profitability.

  Evaluates executive communication of credit-card profitability and partnership analysis. Strong answers lead with recommendation, show assumptions and calculations, bring clear artifacts, summarize risks, and drive next steps.

  Behavioral & LeadershipHR Screen
• Capital OneHard·Data Scientist
  Present and defend your data challenge end-to-end

  This question evaluates end-to-end data science and machine learning competency, focusing on problem definition, data provenance and schema, exploratory data analysis, baselines and modeling choices, validation and metrics, error analysis, fairness and robustness, productionization, and reproducibility.

  Machine LearningHR Screen
• Capital OneMedium·Data Scientist
  Set membership fee under investment constraints

  This question evaluates quantitative skills in pricing optimization, constrained investment allocation, and interpretation of willingness-to-pay curves, testing competency in combining economic reasoning with statistical and mathematical modeling.

  Statistics & MathHR Screen
• CoinbaseMedium·Data Scientist
  Detect sequence rule and repair anomaly

  This question evaluates a candidate's skill in sequence pattern recognition, algorithm design, complexity analysis, and robust integer arithmetic by requiring inference among arithmetic, alternating-difference, geometric, and Fibonacci-plus-constant models while tolerating at most one corrupted term under O(n) time and O(1) extra space constraints.

  Coding & AlgorithmsHR Screen
• CoinbaseMedium·Data Scientist
  Explain handling pressure to bend rules

  This question evaluates a candidate's competency in ethical decision-making, risk assessment, regulatory compliance (including data governance and payroll/immigration constraints), stakeholder influence, and cross-functional communication under pressure.

  Behavioral & LeadershipHR Screen
• CoinbaseMedium·Data Scientist
  Optimize no-penalty test strategy

  This problem tests your ability to model expected value under time constraints by balancing the probability of success per question against a limited resource pool.

  Statistics & MathHR ScreenProbabilityExpected ValueOptimization
• CoinbaseMedium·Data Scientist
  Write SQL and Python for funnels/retention

  This question evaluates SQL and Python data-manipulation skills, including event-to-session joins, temporal window filtering, deduplication, JSON metadata handling, and computation of funnel and retention metrics like daily unique users and conversion rates.

  Data Manipulation (SQL/Python)HR Screen
• DisneyHard·Data Scientist
  Demonstrate influential product leadership under ambiguity

  This question evaluates a candidate's influential product leadership, stakeholder alignment, metric definition, and analytic judgment under ambiguity, including the ability to prioritize trade-offs and drive decisions without formal authority.

  Behavioral & LeadershipHR Screen
• DisneyHard·Data Scientist
  Diagnose and decide on watch-time drop

  This question evaluates a data scientist's skills in causal inference, experiment analytics, metric definition and guardrails, confounder adjustment, and rapid decision-making under time pressure.

  Analytics & ExperimentationHR Screen