Data Scientist Interview Questions

Showing 301–330 of 344 results

• UberMedium·Data Scientist
  Analyze Cancellation Change with Statistics

  This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Apply instrumental variables under interference

  This question evaluates understanding of causal inference with instrumental variables in the presence of interference, testing skills in defining units and aggregation levels for market‑level spillovers, articulating IV assumptions (relevance, exclusion restriction, independence, monotonicity), and formulating estimation frameworks such as two‑stage least squares. Commonly asked in Statistics & Math interviews for data scientist roles because networked marketplaces invalidate simple A/B tests, it sits in the econometrics/causal inference domain and primarily assesses practical application of IV methods while requiring conceptual understanding of identification, robustness diagnostics, and sensitivity analysis.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Compare Two Coin Proportions

  This question evaluates proficiency in statistical inference for proportions, including hypothesis formulation, comparison of two independent samples, confidence interval interpretation, and interpretation of p-values and sampling variability.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Compute A/B sample size under clustering

  This question evaluates competency in experimental design and statistical inference for A/B testing, specifically sample size calculation for two-sample proportions with unequal allocation, clustering effects (ICC and design effect), and adjustments for bot removal and data-quality attrition.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Derive a CDF from a PDF

  This question tests the ability to derive a Cumulative Distribution Function (CDF) from a given Probability Density Function (PDF), ensuring proper integration and boundary conditions.

  Statistics & MathTechnical ScreenProbabilityCalculusStatistics
• UberHard·Data Scientist
  Design an A/B test; choose Z vs T

  This question evaluates a candidate's competency in experimental design and statistical inference, covering sample-size and power calculations, the choice between Z and T tests, sequential testing/alpha-spending strategies, and causal-inference planning for non-randomized rollouts.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Formulate OR model to reduce driver backtracking

  This question evaluates skills in defining a quantitative driver-backtracking metric from GPS and assignment logs, detecting and validating backtracking segments, and formulating constrained optimization models for dispatch and repositioning, drawing on statistics, operations research, and stochastic optimization.

  Statistics & MathOnsite
• UberHard·Data Scientist
  Formulate hypotheses and compute AB test significance

  This question evaluates a data scientist's competency in experimental design and statistical inference for A/B testing, covering hypothesis formulation, difference-in-proportions testing and confidence intervals, guardrail analysis, multiple-testing correction, interim alpha-spending approaches, and variance-reduction techniques such as CUPED.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Measure rider incentive causal ROI

  This question evaluates a data scientist's competency in causal inference and experimental design, marketplace health metric construction, ROI estimation, and the handling of selection bias, spillover effects, multiple testing, and heterogeneity in two-sided platforms.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Should Uber double member discounts?

  This problem tests your ability to design a controlled experiment for a two-sided marketplace and evaluate whether a discount-doubling strategy is statistically and economically viable.

  Statistics & MathTechnical ScreenA/B TestingCausal InferenceMarketplace Economics
• UpstartMedium·Data Scientist
  Analyze HT vs HH stopping-time probabilities

  This question evaluates understanding of discrete probability, stopping-time analysis, pattern occurrence in Bernoulli sequences, derivation of PMFs and expectations, and the ability to generalize results to biased coins.

  Statistics & MathTechnical Screen
• UpstartEasy·Data Scientist
  Assess Probability of Heads in Coin Tosses

  Assess Probability of Heads in Coin Tosses 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
• UpstartEasy·Data Scientist
  Calculate Expected Streaks in Coin Toss Sequence

  Calculate Expected Streaks in Coin Toss Sequence 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
• UpstartEasy·Data Scientist
  Calculate Particle Survival Probability After Time t

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

  Statistics & MathTechnical Screen
• UpstartEasy·Data Scientist
  Combine noisy thermometers; compute random-walk correlations

  This question evaluates estimation and probabilistic reasoning skills, specifically the construction of unbiased estimators and variance-minimizing linear combinations for combining noisy measurements, together with computation and asymptotic characterization of correlations in a 2D simple random walk.

  Statistics & MathOnsite
• UpstartEasy·Data Scientist
  Compute decay, OLS, and classic probability results

  This question evaluates probability and statistical modeling skills—probabilistic reasoning for exponential decay and the coupon-collector expectation, derivation of population OLS coefficients for simple linear models, simulation implementation, and conditional probability reasoning exemplified by the Monty Hall scenario.

  Statistics & MathOnsite
• UpstartEasy·Data Scientist
  Correct length-biased sampling from family-size survey

  This question evaluates understanding of length-biased sampling and inference under sampling bias, specifically how sampling by individuals (children) differs from sampling by households when estimating family-size probabilities.

  Statistics & MathTechnical 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
• UpstartMedium·Data Scientist
  Estimate Family Proportions and Explain Regression Anomalies

  Estimate Family Proportions and Explain Regression Anomalies 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
• UpstartMedium·Data Scientist
  Estimate and Derive Regression Coefficient for X on y

  Estimate and Derive Regression Coefficient for X on y 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
• 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
• UpstartMedium·Data Scientist
  Solve core probability/statistics mini-problems

  Category: Statistics & Math; this prompt evaluates core probability and statistical concepts — exponential decay and discrete survival counts, Bayesian updating, properties of population OLS coefficients, conditional-probability reasoning exemplified by Monty Hall, the coupon-collector expected time, and the conceptual distinction between likelihood and probability — relevant for Data Scientist roles. It is commonly asked because it probes foundational distributional intuition, independence and conditional inference, and expectation/estimation at an introductory-to-intermediate theoretical level that underpins applied modeling and experimental interpretation.

  Statistics & MathOnsite
• UpstartMedium·Data Scientist
  Solve drunk-passenger probability and simulate outcome

  This question evaluates probabilistic reasoning and stochastic-process intuition, including use of invariant arguments, closed-form derivations, statistical estimation via Monte Carlo simulation, and analysis of algorithmic time and space complexity.

  Statistics & MathTechnical Screen
• Virtu FinancialMedium·Data Scientist
  Compute probability in urn with green returns

  Compute probability in urn with green returns 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
• Virtu FinancialMedium·Data Scientist
  Compute probability with colored balls draws

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute probability with colored balls draws states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• VoleonHard·Data Scientist
  Compute robust inference under skew and outliers

  This problem tests your ability to design a robust statistical framework for comparing two groups with highly skewed, heavy-tailed data and potential outliers, moving beyond simple t-tests.

  Statistics & MathTechnical ScreenStatisticsA/B TestingRobust Inference
• VoleonMedium·Data Scientist
  Diagnose and interpret regression assumptions

  This problem tests your ability to diagnose violations of OLS assumptions in count data and determine the appropriate corrective modeling strategy.

  Statistics & MathTechnical ScreenRegressionGLMDiagnostics
• VoleonMedium·Data Scientist
  Explain P-Value Reporting and Bootstrap for Coefficient Estimation

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain P-Value Reporting and Bootstrap for Coefficient Estimation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• VoleonEasy·Data Scientist
  Solve Markov, OLS-rotation, and coin-toss probability

  This multi-part question evaluates understanding of Markov chain and stochastic matrix properties, linear-algebra facts about eigenvalues and stationary distributions, invariance and sampling-distribution properties of OLS under orthonormal rotations, and discrete stopping-time probability reasoning for coin-flip processes.

  Statistics & MathTechnical Screen
• Walmart LabsMedium·Data Scientist
  Understand Propensity Score Matching in Business Analytics

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

  Statistics & MathOnsite