Data Scientist Interview Questions

Showing 1–15 of 15 results

• 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
• 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
  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 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
  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
  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
• GoogleEasy·Data Scientist
  Estimate weather’s effect on mental health

  This problem tests the ability to design an observational study to measure the causal impact of meteorological variables on self-reported mental health scores, focusing on confounding variables and identification strategies.

  Statistics & MathHR ScreenCausal InferenceRegressionConfounding
• J.P. MorganMedium·Data Scientist
  Interpret Model Metrics, Regularization, and Resampling Fundamentals

  This problem tests your understanding of foundational machine learning metrics for imbalanced classification and the theoretical utility of regularization and resampling techniques.

  Statistics & MathHR ScreenStatisticsMachine LearningRegularization
• MetaMedium·Data Scientist
  Analyze ad targeting expectations and distributions

  This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.

  Statistics & MathHR Screen
• Morgan StanleyMedium·Data Scientist
  Solve Market-Risk and Probability Questions

  Morgan Stanley data-scientist HR-screen quant set covering four sub-questions: design a WTI/Brent crude stress-testing framework using heavy-tailed marginals and a t-copula for joint tail dependence; the variance of a sum of correlated normal variables and when the sum is normal; the Monty Hall switching probability; and the eight-balls one-heavier puzzle in two weighings. It probes both conceptual understanding and practical application of dependence modeling, scenario generation, and probabilistic reasoning.

  Statistics & MathHR Screen
• SamsungMedium·Data Scientist
  Estimate laptops in an office

  This question evaluates back-of-the-envelope estimation skills, quantitative reasoning, and the ability to state and justify assumptions when modeling real-world counts like devices.

  Statistics & MathHR Screen
• TikTokMedium·Data Scientist
  Compute cluster-aware significance and sequential corrections

  This question evaluates competency in clustered randomized experiment analysis, including calculation of design effect and effective sample size, cluster-robust inference for differences in proportions, sequential alpha spending (O’Brien–Fleming-style) and comparisons with Bonferroni, Holm–Bonferroni adjustments for multiple guardrail metrics, and Bayesian ROPE interpretation. It is in the Statistics & Math domain and is commonly asked to probe how candidates handle intra-cluster correlation, control Type I error across interim looks and multiple metrics, and demonstrate both conceptual understanding and practical application of power, duration, and multiplicity trade-offs.

  Statistics & MathHR Screen
• UpstartEasy·Data Scientist
  Determine distribution of aX+b when X~N(0,1)

  This problem tests your understanding of how linear (affine) transformations affect the properties of a normal distribution and the derivation of its mean and variance.

  Statistics & MathHR ScreenStatisticsProbabilityNormal Distribution
• UpstartEasy·Data Scientist
  Identify binomial model and compute moments

  This question tests the ability to identify a binomial process, express its probability mass function, and derive its fundamental moments (mean and variance).

  Statistics & MathHR ScreenStatisticsProbabilityBinomial Distribution