Statistics & Math Interview Questions

Showing 301–330 of 439 results

• 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
• Point72Medium·Quantitative Researcher
  Calibrate the Risk-Aversion Parameter

  Calibrate mean-variance risk aversion to target risk and out-of-sample utility while accounting for units, scaling, constraints, and regimes.

  Statistics & MathOnline Assessment
• Point72Medium·Quantitative Researcher
  Choose Practical Portfolio Constraints

  Specify practical stat-arb constraints for neutrality, exposure, concentration, turnover, liquidity, and borrow, including feasibility checks.

  Statistics & MathOnline Assessment
• Point72Medium·Quantitative Researcher
  Derive Annualized Portfolio Volatility

  Derive portfolio volatility from factor covariance and stock-specific variance, then annualize it with consistent horizons and units.

  Statistics & MathOnline Assessment
• Point72Hard·Quantitative Researcher
  Derive the Optimal Bid in a Two-Bidder Second-Price Auction

  Two risk-neutral bidders compete for one item in a sealed-bid second-price auction. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.

  Statistics & MathTechnical Screen
• Point72Medium·Quantitative Researcher
  Estimate Factor Returns and Residual Returns

  Derive weighted factor-return estimates from a sample, then compute residual returns across a larger trading universe with stable linear algebra.

  Statistics & MathOnline Assessment
• Point72Hard·Quantitative Researcher
  Find the Two Heaviest Balls with Minimum Comparisons

  There are 32 balls with distinct weights. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.

  Statistics & MathTechnical Screen
• Point72Medium·Quantitative Researcher
  Formulate a Single-Period Stat-Arb Objective

  Formulate a convex single-period statistical-arbitrage objective balancing expected return against covariance risk, with consistent units.

  Statistics & MathOnline Assessment
• Point72Medium·Quantitative Researcher
  Linearize an L1 Trading Penalty

  Linearize weighted absolute trading costs with auxiliary variables, complete LP constraints, the correct objective sign, and an equivalence proof.

  Statistics & MathOnline Assessment
• Point72Medium·Quantitative Researcher
  Model Trading Frictions in Portfolio Optimization

  Extend a stat-arb objective with unit-consistent commissions, spread, slippage, market impact, turnover, borrow, and concentration costs.

  Statistics & MathOnline Assessment
• 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
• Point72Medium·Quantitative Researcher
  Prove That Lasso Can Be Written as a Quadratic Program

  Derive the Lasso quadratic-program formulation and prove equivalence, convexity, minimizer correspondence, and rank-related uniqueness.

  Statistics & MathTechnical Screen
• Point72Medium·Quantitative Researcher
  Solve Three Probability Models

  Solve three independent probability questions. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.

  Statistics & MathTechnical Screen
• RedditHard·Data Scientist
  Justify synthetic control and handle inference

  This question evaluates understanding of synthetic control methodology, causal identification assumptions, estimation and regularization choices, inference techniques for pointwise and cumulative effects, and diagnostic checks for panel time-series interventions.

  Statistics & MathTechnical Screen
• RobloxHard·Data Scientist
  Apply Bayes' rule to compute posterior

  Evaluates understanding of Bayes’ rule and basic probability concepts—specifically computing a posterior probability P(A|B) and the marginal likelihood P(B) from a prior and likelihoods—in the Statistics & Math domain at a foundational-to-intermediate abstraction level.

  Statistics & MathOnline Assessment
• RobloxEasy·Data Scientist
  Compute A/B test sample size and Bayes posterior

  This question tests the ability to calculate required sample size for a frequentist A/B test using power analysis and to perform a Bayesian update for a Bernoulli process.

  Statistics & MathOnline AssessmentStatisticsA/B TestingBayesian Inference
• RobloxHard·Data Scientist
  Compute sample size and Bayes posterior

  This question evaluates understanding of statistical inference and probabilistic reasoning by combining a frequentist power/sample-size computation for a two-sided two-sample z-test with a basic Bayesian conditional probability calculation.

  Statistics & MathOnline Assessment
• RobloxMedium·Data Scientist
  Derive variance and CTR confidence intervals

  This problem tests your understanding of statistical inference for binomial proportions, specifically calculating confidence intervals for Click-Through Rates (CTR) and understanding how to aggregate variability across multiple days.

  Statistics & MathTechnical ScreenStatisticsInferenceCTR
• RobloxHard·Data Scientist
  Implement robust one/two-sided p-value function

  This question evaluates proficiency in statistical hypothesis testing, numerical computing, and numerical stability when implementing p-value calculations for z and t distributions, and it targets the Statistics & Math domain for a data scientist role.

  Statistics & MathTechnical Screen
• RRokuEasy·Data Scientist
  Explain Bias-Variance Trade-off Simply for Stakeholders

  Evaluates the ability to explain the bias-variance trade-off to non-technical stakeholders. Strong answers define bias as underfitting, variance as overfitting, use a clear analogy, and explain why models must generalize to new data rather than memorize training data.

  Statistics & MathTechnical Screen
• SIG (Susquehanna)Medium·Data Scientist
  Analyze Knights-and-Knaves Dialogue Variants

  Analyze Knights-and-Knaves Dialogue Variants 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
• SIG (Susquehanna)Medium·Quantitative Trader
  Bayesian Posterior That k Red Parts Came From Factory A

  A probability question on Bayesian updating: one of two factories with different red-part rates is chosen at random, and k parts drawn from it are all red. It asks for the conditional probability that the parts came from the first factory, testing likelihoods, priors and careful evaluation of powers.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Calculate Expected Payoff from Two Dice

  This problem tests fundamental knowledge of probability, expected value, and partitioning sample spaces to calculate the average outcome of a stochastic process.

  Statistics & MathOnline AssessmentProbabilityExpected ValueStatistics
• SIG (Susquehanna)Easy·Data Scientist
  Compute Conditional Age Probability Among Cyclists

  This question tests the ability to interpret a contingency table and calculate conditional probabilities by narrowing the sample space to a specific sub-group.

  Statistics & MathOnline AssessmentProbabilityStatisticsContingency Tables
• SIG (Susquehanna)Hard·Data Scientist
  Compute answers to probability and counting puzzles

  This problem tests your ability to model sequential probabilistic events using combinatorics and conditional probability, specifically requiring the calculation of expected outcomes in a game of chance.

  Statistics & MathTechnical ScreenProbabilityExpectationCombinatorics
• SIG (Susquehanna)Medium·Data Scientist
  Compute ruin probability with bold betting

  This question evaluates understanding of probability theory and stochastic processes, focusing on ruin probabilities under a bold betting strategy with state-dependent wagers.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Compute stockout sufficiency probability

  This question evaluates understanding of discrete probability and binomial models for independent Bernoulli trials, testing the ability to compute the probability that a limited resource meets random demand.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Compute the Probability Two Bloom Intervals Overlap

  Calculate the probability that two independent events with different known durations overlap within a fixed time window using geometric probability.

  Statistics & MathOnline AssessmentProbabilityContinuous DistributionsGeometric Probability
• SIG (Susquehanna)Easy·Data Scientist
  Condition on a Chosen Rabbit Losing

  Calculate the conditional probability that a randomly selected rabbit is the least athletic of three rabbits given that it lost its benchmark race, using Bayes' theorem.

  Statistics & MathOnline AssessmentProbabilityBayes' TheoremConditional Probability