Data Scientist Interview Questions

Showing 121–150 of 344 results

• GoogleMedium·Data Scientist
  Estimate population singletons from a 10% log

  This question evaluates mastery of statistical estimation and sampling theory, including recognition of bias from subsampling, frequency‑of‑frequencies modeling for rare events, uncertainty quantification under heavy‑tailed counts, and design of simulation studies.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Estimate unbiased ad scores with many reviewers

  This problem tests the ability to design an estimation framework that accounts for rater bias and variance, specifically applying concepts like hierarchical modeling or fixed-effect normalization to estimate latent variables.

  Statistics & MathTechnical ScreenStatisticsModelingBias
• 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
• GoogleHard·Data Scientist
  Explain Bootstrap and Prove Uniformity

  This question tests the conceptual understanding of bootstrap resampling for variance estimation and the mathematical proof of probability distributions using the properties of the Uniform distribution.

  Statistics & MathTechnical ScreenStatisticsProbabilityBootstrap
• GoogleHard·Data Scientist
  Explain Bootstrap and Statistical Inference

  This problem tests your understanding of bootstrap resampling theory, confidence interval construction, and the statistical trade-offs involved in hypothesis testing for small-sample experiments.

  Statistics & MathTechnical ScreenBootstrapHypothesis TestingMLE
• GoogleMedium·Data Scientist
  Explain P-values and Confidence Intervals in Simple Linear Regression

  Review p-values, confidence intervals and slope inference in simple linear regression, including assumptions and causal limits.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Explain Simpson’s Paradox and Its Causes with Example

  Evaluates Simpson's paradox through definition, confounding, subgroup imbalance, and a numeric reversal example. Strong answers compare stratified and aggregate rates and explain when each comparison matters.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Explain and resolve Simpson’s paradox

  This question evaluates understanding of Simpson's paradox, confounding, and causal reasoning, checking numerical interpretation of subgroup versus aggregate statistics and the ability to recognize when aggregated averages can mislead.

  Statistics & MathTechnical Screen
• GoogleEasy·Data Scientist
  Explain mixed models and fixed vs random effects

  This question evaluates understanding of mixed effects models, the conceptual distinction between fixed and random effects, interpretation of coefficients and variance components, and awareness of practical pitfalls when modeling hierarchical or panel data.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Generate Samples from Truncated Normal Distribution

  Evaluates truncated normal distribution fundamentals and sampling methods for lower-tail truncation. Strong answers name the lower-truncated normal, provide the PDF and CDF, and compare inverse-CDF, rejection, and tail-efficient samplers.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Generate Uniform Samples and Estimate Percentiles

  This problem tests the ability to generate uniform spatial samples using standard random functions and the mathematical application of linear interpolation for percentile estimation from binned data.

  Statistics & MathTechnical ScreenProbabilityStatisticsInterpolation
• GoogleMedium·Data Scientist
  Infer distribution and choose robust statistics

  This problem tests the ability to diagnose non-normal data distributions (specifically zero-inflated, heavy-tailed data) and select appropriate statistical metrics for hypothesis testing.

  Statistics & MathTechnical ScreenStatisticsData DistributionsHypothesis Testing
• GoogleMedium·Data Scientist
  Interpreting a Dosage-Recovery Regression: P-values, CIs, Causality

  Given a fitted dosage-vs-recovery-time regression, explain exactly what the reported p-value and confidence interval mean, what assumptions back that inference, how a confidence interval differs from a prediction interval, and why the result alone cannot establish causality. Tests statistical precision under pressure.

  Statistics & MathOnsitestatisticslinear-regressionhypothesis-testing
• GoogleMedium·Data Scientist
  Measure Bird Species Segregation

  This question evaluates spatial statistical reasoning, ecological data analysis, and the ability to formulate hypotheses, choose appropriate segregation metrics, and account for confounders and sampling bias.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Narrow a confidence interval for a mean

  This question evaluates understanding of confidence intervals, the relationship between sample size and interval width, variance-reduction techniques, the impact of changing confidence levels, and the ability to communicate statistical risk and design-based trade-offs.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Prove OLS invariance to linear transforms

  This question evaluates understanding of linear regression invariance under invertible linear transformations, multivariate linear algebra, and the impact of regularization (ridge and lasso) on coefficient and prediction stability in the Statistics & Math domain.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Reading a Regression Slope: P-values, Intervals, and Causality

  Use a fitted slope from a one-predictor regression to explain precisely what a p-value and a confidence interval mean, what assumptions they rest on, how confidence and prediction intervals differ, and why significance is not causation. Tests statistical precision under interview pressure.

  Statistics & MathOnsitestatisticslinear-regressionhypothesis-testing
• GoogleMedium·Data Scientist
  Test a coefficient and explain t-distribution

  This question evaluates a candidate's competence in statistical inference for linear regression—specifically hypothesis testing for OLS coefficients, construction of t-statistics, estimation of standard errors, and the role of estimated error variance—within the Statistics & Math domain for Data Scientist roles.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Test if one value comes from N(μ,σ²)

  This question tests the ability to perform statistical hypothesis testing on a single observation, evaluate the limitations of small sample sizes, and explain the trade-offs between Type I and Type II errors.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingNormal Distribution
• GoogleMedium·Data Scientist
  Understand Simpson's Paradox with Simple Examples

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand Simpson's Paradox with Simple Examples states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• GustoMedium·Data Scientist
  Compute Parking Clustering Probability and P-Value

  This question tests your ability to calculate exact combinatorial probabilities under a null hypothesis and define a p-value for a spatial clustering test.

  Statistics & MathTechnical ScreenProbabilityCombinatoricsHypothesis Testing
• HHeadlandsMedium·Data Scientist
  Compare Candy-Color Guessing Experiments with Different Sample Sizes

  Evaluate and compare evidence from two binomial experiments with different sample sizes and hit rates against a random guessing null hypothesis.

  Statistics & MathTechnical Screenstatisticshypothesis-testingbinomial-distribution
• HHeadlandsMedium·Data Scientist
  t-Stat With an Added Orthogonal Regressor, Live Sharpe Check, and Absolute-Loss Estimator

  Solve three core quantitative problems covering OLS regression mechanics under orthogonal regressors, statistical significance of backtested Sharpe ratios in live deployment, and loss-function minimization for L1 error.

  Statistics & MathOnline AssessmentEconometricsSharpe RatioLinear Regression
• Hudson River TradingMedium·Data Scientist
  Compare Mean and Median for a Distribution with a Point Mass

  This question tests the theoretical and empirical comparison between the sample mean and sample median on a mixture distribution containing a discrete point mass and a Gaussian component.

  Statistics & MathTechnical Screenstatisticsprobabilityestimation
• IBMEasy·Data Scientist
  How do you detect and fix multicollinearity?

  This question evaluates understanding of multicollinearity and related competencies in regression diagnostics, feature engineering, model interpretability, and the trade-offs between coefficient inference and predictive performance.

  Statistics & MathTechnical Screen
• InstacartHard·Data Scientist
  Choose tests under non‑normal, unequal variance

  This question tests the ability to select and justify statistical inference methods for metrics that violate standard parametric assumptions, specifically when dealing with heavy-tailed, heteroskedastic, and zero-inflated distributions.

  Statistics & MathOnsiteStatisticsA/B TestingInference
• IEIntercontinental ExchangeMedium·Data Scientist
  Analyze Newton-Raphson Convergence, Choose Sigma, and Derive Vega

  This question evaluates numerical root-finding and option pricing competencies, specifically understanding Newton–Raphson convergence properties, implied volatility inference under the Black–Scholes model, and option sensitivity (Vega).

  Statistics & MathTechnical Screen
• 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
• JGJain GlobalEasy·Data Scientist
  Define the p-Value, Then Explain How to Test Two Price Series for Cointegration

  Two statistics questions from a systematic trading screen: give a precise definition of the p-value and its common misreadings, then explain what cointegration is and how to test two price series for it. It tests hypothesis-testing rigor, stationarity, the difference between correlation and cointegration, and multiple-testing pitfalls.

  Statistics & MathTechnical Screen
• JJefferiesEasy·Data Scientist
  How to compute correlation from variances

  Practice deriving correlation from variance identities for two random variables. The solution explains why Var(X+Y) and Var(X) alone are insufficient, derives covariance and correlation when Var(Y) is known, simplifies the equal-variance case, and checks edge cases where correlation is invalid or inconsistent.

  Statistics & MathTechnical Screen