Data Scientist Interview Questions

Showing 211–240 of 344 results

• MicrosoftEasy·Data Scientist
  Use confusion matrix to choose model metric

  This question tests the ability to select appropriate classification metrics based on specific business objectives, asymmetric error costs, and dataset characteristics like class imbalance.

  Statistics & MathTechnical ScreenClassificationImbalanced DataEvaluation Metrics
• 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
• MMorningstarEasy·Data Scientist
  Explain a stochastic process simply

  This question assesses the candidate's ability to intuitively define a stochastic process, differentiate it from static probability models, and articulate fundamental properties like stationarity and the Markov property.

  Statistics & MathTechnical ScreenStatisticsStochastic ProcessesTime Series
• NetflixMedium·Data Scientist
  Answer core probability and statistics questions

  This question evaluates core probability and statistical inference competencies—covering Bayesian reasoning, regression controls and confounding, the Central Limit Theorem, distribution moments, t-statistics, and effect size/MDE—relevant to quantitative data analysis and hypothesis testing for a Data Scientist role in the Statistics & Math domain.

  Statistics & MathOnsite
• NetflixHard·Data Scientist
  Evaluate Propensity Score Matching Alternatives and Diagnostics

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

  Statistics & MathOther
• NetflixEasy·Data Scientist
  Solve core probability and statistics questions

  This problem tests your understanding of the Central Limit Theorem and its application in power analysis for A/B testing and confidence interval estimation.

  Statistics & MathOnsiteProbabilityA/B TestingCentral Limit Theorem
• OneMain FinancialMedium·Data Scientist
  Differentiate and control Type I/II errors

  This question evaluates understanding of hypothesis testing fundamentals such as Type I/Type II errors, statistical power and sample size calculation for two-proportion tests, family-wise error rate control (e.g., Bonferroni), and sequential analysis/alpha-spending schemes.

  Statistics & MathTechnical Screen
• OneMain FinancialMedium·Data Scientist
  Maximize Probability of Drawing Two Red Balls

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

  Statistics & MathTechnical Screen
• OptiverHard·Data Scientist
  Analyze EV, arbitrage, and bet sizing across games

  This question evaluates proficiency in probability and statistics—particularly expected value and variance computation, arbitrage detection across payoff tables, quantitative bet-sizing strategies (e.g., Kelly and fractional sizing), and numerical approximation skills for independent discrete outcomes.

  Statistics & MathTechnical Screen
• OptiverMedium·Data Scientist
  Compute expected coin flips to meet on octagon

  Compute expected coin flips to meet on octagon 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
• OptiverEasy·Data Scientist
  Compute probabilities and expectations in random processes

  This question evaluates proficiency in probability theory, combinatorics, and mathematical expectation through a series of rapid-fire brain-teasers. It tests the ability to set up clean probabilistic models, apply the right technique, and derive exact closed-form answers under time pressure — skills commonly assessed in quantitative and data science interviews.

  Statistics & MathOnline Assessment
• OptiverHard·Data Scientist
  Find next terms in sequences

  This question evaluates pattern recognition, numerical reasoning, and sequence-inference competency across arithmetic, fractional, and recurrence-style relationships, and falls under the Statistics & Math domain relevant for data scientist roles.

  Statistics & MathOnline Assessment
• OptiverHard·Data Scientist
  Solve probability and expectation problems

  This problem tests your ability to calculate expected stopping times in a discrete random walk and your understanding of conditional expectation and symmetry in probability.

  Statistics & MathOnline AssessmentProbabilityExpectationStochastic Processes
• OOtherMedium·Data Scientist
  Compute and estimate Markov transition probabilities

  This question evaluates understanding of discrete-time Markov chains, multi-step transition probabilities and empirical estimation of transition matrices, testing probabilistic reasoning and matrix-based computation in the Statistics & Math domain for a Data Scientist role; it is commonly asked because it probes both theoretical grasp of stochastic processes and practical data-driven parameter estimation. At an applied algorithmic abstraction level, it requires explicit mapping of state labels to matrix indices, computing k-step transitions from a given row-stochastic matrix, estimating transition probabilities from observed one-step transitions, and addressing zero-outgoing-observation cases and any chosen smoothing policy (or justification for none).

  Statistics & MathTechnical Screen
• OOtherEasy·Data Scientist
  Compute multi-step Markov probabilities and estimate transitions

  This question evaluates understanding of discrete-time Markov chains, computation of multi-step transition probabilities from a one-step transition matrix, and empirical estimation of transition matrices from observed state-pair data including handling states with zero outgoing transitions.

  Statistics & MathTechnical Screen
• OOtherHard·Data Scientist
  Decide launch with p=0.10 at alpha 0.05

  This question evaluates a data scientist's competency in statistical inference and experiment decision-making, including frequentist hypothesis testing, conditional power and sample-size planning, Bayesian updating, and cost-sensitive risk analysis.

  Statistics & MathTechnical Screen
• OOtherMedium·Data Scientist
  Diagnose and fix linear regression violations

  Work through an OLS model on 10,000 observations: state the Gauss-Markov assumptions, show why residuals are orthogonal to the predictors, and handle three correlated predictors. Decide which violations break unbiasedness versus only inference, and what fixes apply.

  Statistics & MathOnsite
• OOtherMedium·Data Scientist
  Simulate via inverse transform and Gibbs

  This question evaluates understanding of random variate generation and Markov chain Monte Carlo techniques, specifically inverse-transform sampling for the Logistic(μ,s) distribution and Gibbs sampling for a bivariate joint density, testing competency in probability theory, distribution properties, and statistical computing within the Statistics & Math domain. It is commonly asked to assess the ability to derive inverse CDFs and full conditional distributions, verify sample correctness with goodness-of-fit and convergence diagnostics, and reason about sample size, burn-in, and thinning, reflecting a mix of conceptual understanding and practical application.

  Statistics & MathOnsite
• PayPalEasy·Data Scientist
  Calculate Probability of Heads in Coin Flip Experiment

  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 in Coin Flip Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• PayPalEasy·Data Scientist
  Decide if a dice-betting game is favorable

  This question evaluates understanding of probability theory, expected value computation, risk-neutral decision criteria, and basic Monte Carlo simulation concepts within the Statistics & Math domain, testing both conceptual understanding of discrete probabilistic outcomes and practical application of simulation-based verification.

  Statistics & MathOnsite
• PayPalEasy·Data Scientist
  Explain confounding with an Uber example

  This Statistics & Math interview question for a Data Scientist tests understanding of confounding and causal inference in observational data. Candidates must define a confounder, give a concrete non-demographic Uber example identifying the exposure, outcome, and confounder with the direction of bias, and describe at least two mitigation methods along with the assumptions each requires.

  Statistics & MathOnsite
• PayPalEasy·Data Scientist
  Explain p-values and interpret regressions

  A PayPal Data Scientist onsite statistics rapid-fire covering p-values (plain-language and formal definitions, common misinterpretations, and an A/B-test decision at p=0.03), interpreting linear-regression coefficients under confounding (associational vs causal, 'controlling for' failure modes, and bias-reduction methods), and L1 vs L2 regularization with how to choose the penalty strength. It tests both statistical depth and the ability to communicate to product and technical audiences.

  Statistics & MathOnsite
• PayPalHard·Data Scientist
  Interpret p-values and common pitfalls

  Evaluates interpretation of p-values, hypothesis testing, experimental design and statistical pitfalls in a fraud/experimentation context—including concepts like effect size, statistical power, multiple comparisons, delayed/rare outcomes, and imperfect randomization; category/domain: Statistics & Math.

  Statistics & MathTechnical Screen
• PayPalEasy·Data Scientist
  Should you play a dice payout game?

  This question evaluates understanding of probability, expected value, and payoff-driven decision-making by requiring computation of expected return from discrete game outcomes. It is commonly asked to assess quantitative reasoning in the Statistics & Math category and primarily tests conceptual understanding of expected value and probabilistic reasoning rather than practical implementation.

  Statistics & MathOnsite
• PinterestHard·Data Scientist
  Analyze survey with gender imbalance

  Analyze survey with gender imbalance 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
• PinterestHard·Data Scientist
  Connect Probability, Experiment Sizing, and Bootstrap Inference

  Tests end-to-end statistical rigor across discrete probability modeling, sample size derivation for A/B testing, bootstrap confidence intervals for heavy-tailed engagement metrics, and translating conflicting inferential signals into product launch recommendations.

  Statistics & MathOnsiteStatisticsExperimentationA/B Testing
• PinterestHard·Data Scientist
  Design rigorous A/B test and causal analysis

  This question evaluates a data scientist's competency in experimental design, sample-size and power calculations, variance-reduction methods (e.g., CUPED), sequential testing and alpha spending, clustering and interference effects, SRM checks, and causal identification strategies such as DID, IV, and RDD within the Statistics & Math domain.

  Statistics & MathOnsite
• PinterestHard·Data Scientist
  Estimate billboard reach and impressions

  This question evaluates probabilistic modeling and statistical inference skills applied to audience measurement and attribution, covering Poisson-based frequency modeling, visibility-adjusted impressions, panel-based deduplication and expansion, uncertainty propagation (delta method/bootstrap), and Markov-chain attribution within the Statistics & Math / Data Science domain. It is commonly asked to test the ability to convert traffic and visibility inputs into quantitative reach and impressions with propagated confidence intervals, reason about attribution via a 3-state Markov chain, identify measurement biases, and demonstrates both conceptual understanding and practical application through numerical estimation and sensitivity analysis.

  Statistics & MathOnsite
• PinterestMedium·Data Scientist
  Implement Bootstrap and Jackknife Estimates on a DataFrame

  Tests practical statistical computing skills by requiring the implementation of bootstrap confidence intervals and jackknife variance estimation for arbitrary metrics evaluated on pandas DataFrames.

  Statistics & MathOnsitestatisticsresamplingbootstrap
• Point72Hard·Data Scientist
  Price a coin-doubling game rationally

  This question evaluates understanding of probability distributions, divergent expected values, utility theory, and optimal betting strategies (Kelly staking) within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathOnline Assessment