Data Scientist Interview Questions

Showing 1–30 of 344 results

• AdobeEasy·Data Scientist
  Understand P-Value's Role in Product Decision-Making

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand P-Value's Role in Product Decision-Making states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• AirbnbMedium·Data Scientist
  Test conversion difference and adjust for clustering

  This question evaluates proficiency in statistical inference for A/B testing—estimating and comparing conversion proportions, conducting two-sided hypothesis tests, adjusting for day-level clustering using ICC and design-effect corrections, and performing power and sample-size calculations; it belongs to the Statistics & Math domain for a Data Scientist role and combines conceptual understanding with practical application. It is commonly asked to assess an interviewee's ability to interpret conversion uplift under realistic experimental constraints, account for intra-cluster correlation when estimating effective sample sizes and uncertainty, and reason about experiment duration and robustness checks.

  Statistics & MathTechnical Screen
• AmazonEasy·Data Scientist
  Answer core probability and inference questions

  Tests fundamental statistical inference and probability concepts essential for experimentation and data analysis, including the Central Limit Theorem, hypothesis testing errors, and causal reasoning.

  Statistics & MathTechnical ScreenStatisticsA/B TestingHypothesis Testing
• AmazonMedium·Data Scientist
  Calculate A/B sample size, CI, decision rules

  This question evaluates skills in experimental design and statistical inference, including sample size calculation, hypothesis testing, confidence interval interpretation, multiple comparison control, variance reduction techniques, and operational considerations for A/B testing.

  Statistics & MathOnsite
• AmazonMedium·Data Scientist
  Compute an A/B test p-value by hand

  This question evaluates understanding of hypothesis testing for proportions, specifically the two-proportion z-test, p-value computation and interpretation of statistical significance in an A/B test with binary outcomes.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Compute and interpret quantile loss vs RMSE

  This question tests the ability to distinguish between point-prediction error metrics and quantile loss, and the practical implications of choosing an evaluation metric in the context of asymmetric business costs.

  Statistics & MathTechnical ScreenStatisticsForecastingLoss Functions
• AmazonMedium·Data Scientist
  Compute p-values, CIs, and adjust multiples

  This question evaluates a candidate's proficiency in statistical inference, covering hypothesis testing and test selection, confidence interval construction, multiple-testing corrections, error types, and asymptotic reasoning.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Determine 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
• AmazonHard·Data Scientist
  Estimate 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
• AmazonMedium·Data Scientist
  Explain P-value, Confidence Interval, and Multiple Testing Adjustments

  Evaluates A/B testing inference fundamentals, including p-values, confidence intervals, multiple testing adjustments, Type I and Type II errors, z-tests versus t-tests, and CLT versus LLN. Strong answers connect definitions to experiment decisions and common pitfalls.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Propensity Score Matching and Assess Covariate Balance

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Propensity Score Matching and Assess Covariate Balance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Statistical Outputs to Non-Technical Stakeholders

  Explain Statistical Outputs to Non-Technical Stakeholders 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 & MathTechnical Screen
• AmazonMedium·Data Scientist
  Identify 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
• AmazonHard·Data Scientist
  Plan and analyze an A/B test

  This question tests the ability to design a statistically rigorous A/B test by accounting for clustered randomization, sequential testing boundaries, and multiple comparison corrections.

  Statistics & MathTechnical ScreenExperimental DesignPower AnalysisClustered Data
• 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
• AppleMedium·Data Scientist
  Compare Normal and Poisson Distributions in Statistics

  Evaluates statistical understanding of Normal and Poisson distributions for counts and continuous measurements. Strong answers compare support, parameters, use cases, Normal approximation conditions, and mean-variance derivations.

  Statistics & MathTechnical Screen
• AppleMedium·Data Scientist
  Compare Normal vs Poisson; test dispersion and approximate tails

  This question evaluates a data scientist's competency in statistical inference for count data, covering MLE and asymptotic variance estimation, Wald confidence intervals, tests for equidispersion versus overdispersion, Normal approximation with continuity correction for Poisson tails, and model selection diagnostics.

  Statistics & MathTechnical Screen
• AppleMedium·Data Scientist
  Differentiate P-value and Confidence Interval in Statistics

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Differentiate P-value and Confidence Interval in Statistics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• AppleEasy·Data Scientist
  How would you critique this regression?

  An Apple Data Scientist technical-screen statistics question that asks you to critique another analyst's linear-regression workflow for modeling website dwell time. It tests OLS assumptions (and why marginal normality of Y is not one of them), model-selection and overfitting bias from exhaustive interaction search, diagnostics and validation, and the distinction between prediction, inference, and causal goals on skewed outcome data.

  Statistics & MathTechnical Screen
• BBarclaysEasy·Data Scientist
  Determine Worker Counts from Production Ratios

  This problem tests mathematical modeling and systems of linear equations by requiring the candidate to derive specific headcount requirements based on production demand and individual productivity constraints.

  Statistics & MathOnline AssessmentAlgebraSystems of EquationsOptimization
• BBlackEdgeEasy·Data Scientist
  Solve quantitative word and algebra problems

  This question evaluates quantitative reasoning and arithmetic/algebraic manipulation, covering unit conversion, percentage change, systems of linear equations, time-rate calculations, and series/summation properties.

  Statistics & MathTechnical Screen
• Boston Consulting GroupEasy·Data Scientist
  Calculate Probability and Statistics for Dice Roll Outcomes

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability and Statistics for Dice Roll Outcomes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnline Assessment
• Boston Consulting GroupEasy·Data Scientist
  Calculate Probability of Heads and Red Balls Drawing

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads and Red Balls Drawing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnline Assessment
• Boston Consulting GroupMedium·Data Scientist
  Compute posterior and predictive coin probabilities

  This question evaluates Bayesian inference and probabilistic reasoning, focusing on posterior updating, posterior predictive probability, and the conditional expectation of a stopping time given observed outcomes.

  Statistics & MathOnline Assessment
• BByteDanceMedium·Data Scientist
  Calculate Triangle Probability From Broken Stick

  This question evaluates a candidate's understanding of geometric probability, continuous random variables, and spatial reasoning within probabilistic models.

  Statistics & MathTechnical Screen
• BByteDanceMedium·Data Scientist
  Explain Type I/II errors vs precision/recall

  This question evaluates understanding of Type I and Type II errors in hypothesis testing and binary classification, their correspondence to false positives and false negatives, and how those concepts relate to precision and recall within the Statistics & Math domain.

  Statistics & MathTechnical Screen
• BByteDanceHard·Data Scientist
  Interpret and validate regression with interactions

  This question evaluates understanding of regression modeling with interaction terms, binary outcome models and odds/marginal-effect interpretation, causal inference concepts like endogeneity and instrumental variables, model diagnostics and standard-error choices, and count-data modeling.

  Statistics & MathTechnical Screen
• C3 AIMedium·Data Scientist
  Explain Training R-Squared After Adding a Random Feature

  Prove that ordinary training R-squared cannot decrease when adding a feature, while adjusted and test-set R-squared can.

  Statistics & MathOnline Assessment
• C3 AIMedium·Data Scientist
  Explain the Gaussian Distribution and Its Limits

  Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.

  Statistics & MathTechnical ScreenStatisticsProbabilityAnomaly Detection
• CVS HealthMedium·Data Scientist
  Compute A/B significance, CI, and power

  This question evaluates proficiency in statistical inference for A/B testing—specifically two-proportion z-tests, construction of confidence intervals with unpooled standard errors, sample-size and power calculations, and multiple-comparison correction via Bonferroni.

  Statistics & MathOnline Assessment