Statistics & Math Interview Questions

Showing 391–420 of 439 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 FinancialHard·Quantitative Researcher
  Approximate a Binomial Upper Tail with a Normal Distribution

  This question tests the application of the Normal approximation to the Binomial distribution, the use of continuity correction, and the ability to evaluate the error bounds of this approximation.

  Statistics & MathOnsiteStatisticsProbabilityNormal Approximation
• Virtu FinancialHard·Quantitative Researcher
  Approximate a Rare-Event Binomial Probability

  This problem tests the application of the Poisson approximation to binomial distributions for rare events and the evaluation of the error bounds associated with this approximation.

  Statistics & MathOnsiteProbabilityPoisson DistributionBinomial Distribution
• Virtu FinancialHard·Quantitative Researcher
  Birthday Collisions and the Closest Pair of Birthdays

  This problem tests your understanding of probability theory, specifically the generalized birthday paradox, and your ability to analyze spacing distributions of random variables.

  Statistics & MathOnsiteProbabilityStatisticsCombinatorics
• Virtu FinancialHard·Quantitative Researcher
  Compare Two Strategies for Getting Ten Consecutive Heads

  This problem tests your ability to model probability through recursion and expectation, specifically comparing the efficiency of two distinct strategies for achieving a target streak of coin tosses.

  Statistics & MathOnsiteProbabilityExpectationRecurrence
• 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
• VoleonMedium·Quantitative Researcher
  Bound R-Squared for Individual and Combined OLS Regressions

  Derive in-sample OLS R-squared bounds and the joint-model range from individual scores, including intercept assumptions, predictor correlation, and out-of-sample exceptions.

  Statistics & MathTechnical Screen