Statistics & Math interview questions

Showing 271–300 of 439 results

• Compute negative-price probability after n stepsMedium

  This question evaluates understanding of discrete probability distributions and random-walk processes within Statistics & Math, testing skills in binomial modeling and numeric computation for probabilistic outcomes.

  OptiverSoftware EngineerStatistics & MathOnline Assessment
• Compute odds under time pressureMedium

  This question evaluates probabilistic reasoning and combinatorial calculation skills, including conditional probability, distributional thinking and the ability to convert probabilities into odds.

  OptiverQuantitative TraderStatistics & MathOnline Assessment
• Compute permutations with repeated lettersEasy

  This question evaluates combinatorics and counting principles within the Statistics & Math domain, focusing on permutations with repeated characters and the generalization to multiple symbol counts.

  OptiverSoftware EngineerStatistics & MathOnline Assessment
• Compute probabilities and expectations in random processesEasy

  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.

  OptiverData ScientistStatistics & MathOnline Assessment
• Demonstrate task switching and memory strategiesMedium

  This question evaluates task-switching ability, working-memory management, interference control, and error-recovery strategies—core executive functions involved in rapid stimulus–response mappings.

  OptiverSoftware EngineerStatistics & MathTechnical Screen
• Design dice betting strategy under time limitMedium

  This question evaluates probabilistic reasoning, expected-value computation, risk management and staking-strategy concepts including Kelly-style bet sizing, confidence-interval estimation, and return–variance trade-offs in the Statistics & Math domain.

  OptiverQuantitative ResearcherStatistics & MathTechnical Screen
• Find next terms in sequencesHard

  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.

  OptiverData ScientistStatistics & MathOnline Assessment
• Find the Color Probability After Discarding CardsHard

  This problem tests your understanding of conditional probability, combinatorics, and how dependent events (sampling without replacement) impact statistical expectations.

  OptiverSoftware EngineerStatistics & MathTechnical Screen
• Find the Probability That the Three Smallest Dice Sum to ThreeHard

  This problem tests combinatorics and probability theory by requiring the derivation of the exact probability that the three smallest values of four independent dice rolls sum to exactly three.

  OptiverSoftware EngineerStatistics & MathTechnical Screen
• Optimize interval-scoring strategyHard

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

  OptiverQuantitative TraderStatistics & MathTechnical Screen
• Plan for timed probability assessmentMedium

  This question evaluates rapid probabilistic and statistical reasoning, time management and problem prioritization under strict timing, along with the ability to adapt approaches based on scoring rules and calculator restrictions, and it is classified in the Statistics & Math domain.

  OptiverSoftware EngineerStatistics & MathOnline Assessment
• Solve numeric sequence pattern puzzlesMedium

  This question evaluates pattern recognition and quantitative reasoning skills, requiring identification of numeric relationships, series properties, and simple number-theoretic or arithmetic transformations.

  OptiverSoftware EngineerStatistics & MathOnline Assessment
• Solve probability and expectation problemsHard

  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.

  OptiverData ScientistStatistics & MathOnline Assessment
• Solve quick probability questionsMedium

  This set evaluates core probability and combinatorics skills including discrete distributions, conditional probability, independence, expectation and variance, and basic stochastic processes such as gambler's ruin, situated in the Statistics & Math domain.

  OptiverSoftware EngineerStatistics & MathOnline Assessment
• Compute and estimate Markov transition probabilitiesMedium

  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).

  OtherData ScientistStatistics & MathTechnical Screen
• Compute multi-step Markov probabilities and estimate transitionsEasy

  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.

  OtherData ScientistStatistics & MathTechnical Screen
• Decide launch with p=0.10 at alpha 0.05Hard

  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.

  OtherData ScientistStatistics & MathTechnical Screen
• Diagnose and fix linear regression violationsMedium

  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.

  OtherData ScientistStatistics & MathOnsite
• Optimal Stopping with a Ten-Sided Die and a ResetMedium

  Derive optimal stopping thresholds for a fair ten-sided accumulation game, then account for the continuation value of a one-time zero reset.

  OtherQuantitative ResearcherStatistics & MathTechnical Screen
• Simulate via inverse transform and GibbsMedium

  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.

  OtherData ScientistStatistics & MathOnsite
• Solve a Set of Probability and Quantitative Reasoning ProblemsMedium

  Work through fourteen quantitative interview problems covering probability, counting, expectation, algebra, logical constraints, and rate reasoning. Each result should be auditable, use the correct sample space, and state any interpretation that affects the calculation.

  OtherSoftware EngineerStatistics & MathOnline Assessment
• Calculate Probability of Heads in Coin Flip ExperimentEasy

  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.

  PayPalData ScientistStatistics & MathOnsite
• Decide if a dice-betting game is favorableEasy

  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.

  PayPalData ScientistStatistics & MathOnsite
• Explain confounding with an Uber exampleEasy

  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.

  PayPalData ScientistStatistics & MathOnsite
• Explain p-values and interpret regressionsEasy

  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.

  PayPalData ScientistStatistics & MathOnsite
• Interpret p-values and common pitfallsHard

  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.

  PayPalData ScientistStatistics & MathTechnical Screen
• Should you play a dice payout game?Easy

  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.

  PayPalData ScientistStatistics & MathOnsite
• Analyze survey with gender imbalanceHard

  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.

  PinterestData ScientistStatistics & MathOnsite
• Connect Probability, Experiment Sizing, and Bootstrap InferenceHard

  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.

  PinterestData ScientistStatistics & MathOnsite
• Design rigorous A/B test and causal analysisHard

  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.

  PinterestData ScientistStatistics & MathOnsite