Data Scientist Interview Questions

Showing 151–180 of 344 results

• JJefferiesMedium·Data Scientist
  How to derive correlation from variances

  Evaluates derivation of correlation from variance identities for two random variables. Strong answers explain why Var(X+Y) and Var(X) alone do not identify correlation, then derive covariance and correlation when Var(Y) is also known.

  Statistics & MathTechnical Screen
• LinkedInEasy·Data Scientist
  Choose better bank queue and describe distributions

  This question tests your ability to apply basic queueing theory to compare waiting times and your ability to reason about how distributional shape affects central tendency measures.

  Statistics & MathTechnical ScreenQueueing TheoryProbabilityStatistics
• LinkedInMedium·Data Scientist
  Compare queueing systems and common distributions

  LinkedIn data scientist statistics screen covering pooled versus separate queues, height distributions and mixture distributions, right-skewed network degree, mean-median-mode ordering, and L1/L2 regularization bias.

  Statistics & MathTechnical Screen
• LinkedInMedium·Data Scientist
  Derive expectation for two consecutive heads

  This question evaluates mastery of probability theory and stochastic processes, specifically expectation and variance calculations, Markov-chain or recurrence reasoning, and pattern waiting times in Bernoulli trials relevant for a Data Scientist role.

  Statistics & MathTechnical Screen
• LinkedInMedium·Data Scientist
  Derive mean and variance of x̄

  This question tests your ability to derive the expected value and variance of the sample mean for a sequence of random variables, accounting for the effect of correlation between them.

  Statistics & MathTechnical ScreenStatisticsVarianceExpectation
• LinkedInMedium·Data Scientist
  Estimate an Experiment’s Mean Effect from Cohort and Metric Tables

  Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.

  Statistics & MathOnsite
• LinkedInHard·Data Scientist
  Measure Causal Impact of Self-Selected App Redesign

  LinkedIn causal inference prompt on measuring a self-selected app redesign, covering potential outcomes, ATT, propensity matching or weighting, staggered difference-in-differences, event studies, covariates, balance, and pre-trend validation.

  Statistics & MathOnsite
• LyftMedium·Data Scientist
  Analyze Rider Behavior in Dynamic-Pricing Trial

  Analyze Rider Behavior in Dynamic-Pricing Trial 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
• LyftMedium·Data Scientist
  Commuter Ride-Choice Probability: Committed and Permanent Riders

  Tests discrete probability modeling, complementary counting, and Bayesian updating to analyze multi-day commuter behavior across distinct rider segments.

  Statistics & MathTechnical Screenprobabilitybayes-theoremconditional-probability
• LyftMedium·Data Scientist
  Compute Commuter Ride Probabilities

  This question tests the ability to apply Bayes' Theorem and marginal probability to model commuter behavior and predict ride-hailing demand based on environmental factors.

  Statistics & MathTechnical ScreenProbabilityBayesian InferenceConditional Probability
• LyftMedium·Data Scientist
  Compute Poisson supply–demand match probability

  This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.

  Statistics & MathOnsite
• LyftEasy·Data Scientist
  Determine Probability of Single Ride on Following Day

  Evaluates a binomial probability setup for a ride-pricing experiment with two independent offers. Strong answers model the number of reasonable prices as Binomial(2, P), compute exactly one success as 2P(1-P), and compute permanent-rider probability as P squared.

  Statistics & MathTechnical Screen
• LyftEasy·Data Scientist
  Explain Bayes’ Theorem and P-Value in Decision-Making

  Explain Bayes’ Theorem and P-Value in Decision-Making 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
• MetaMedium·Data Scientist
  Analyze User Comment Distribution and Sampling Effects

  Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Analyze User Transfer Distribution in Initial Launch Period

  Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Analyze ad targeting expectations and distributions

  This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.

  Statistics & MathHR Screen
• MetaMedium·Data Scientist
  Analyze skewed comments and sampling effects

  This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Annotating and forecasting a long‑tail distribution

  Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.

  Statistics & MathOnsite
• MetaHard·Data Scientist
  Apply sequential testing without p-hacking

  This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Calculate Ad Insertion Statistics for Two Methods

  Meta statistics prompt comparing two ad-insertion strategies, covering binomial moments, deterministic quotas, tail probability for excessive ads, adjacent ad pairs, and risk of long ad runs.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Calculate Expected Impressions and Probability for Users

  Meta probability question on random ad-impression allocation, including binomial expectation, probability a user sees at least one impression, expected unique reach, and large-population approximations.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Calculate Posterior Fraud Probability Using Bayes' Theorem

  Calculate Posterior Fraud Probability Using Bayes' Theorem 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
• MetaEasy·Data Scientist
  Calculate Posterior Probability of Flagged User Being Bad Actor

  Evaluates Bayesian inference and confusion-matrix reasoning for abuse detection. Strong answers derive posterior precision from prevalence, TPR, and FPR, define Type I and Type II errors, and compute true positives and false positives for 10 million users.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Calculate Probability of Honest and Relevant Chatbot Answers

  Calculate Probability of Honest and Relevant Chatbot Answers 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
• MetaMedium·Data Scientist
  Characterize and compare transfer-count distributions over time

  This question evaluates a candidate's ability to model zero-inflated, heavy-tailed count data, interpret distributional summaries and percentiles, reason about temporal shifts from retention and fraud dynamics, and identify robust summary statistics and diagnostics for a Data Scientist role.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Characterize metric distribution and quantiles

  This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Compare Ad-Insertion Strategies: Expected Ads and Probabilities

  Evaluates probability, expectation, and variance for newsfeed ad-insertion strategies. Strong answers compute expected ad load, at-least-one exposure probability, and explain why equal expected counts can still produce different user experiences.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compare Bayesian and frequentist decisions

  This question evaluates understanding of Bayesian inference versus frequentist hypothesis testing, decision theory under asymmetric loss, sequential monitoring/optional stopping, and prior elicitation applied to A/B testing within the Statistics & Math domain.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Compute Bayes probability for fake accounts

  This question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.

  Statistics & MathOnsiteBayes TheoremProbabilityStatistics
• MetaMedium·Data Scientist
  Compute and correct correlation significance inflation

  This question tests the understanding of how multiple hypothesis testing increases the risk of false positives (Type I error) in high-dimensional correlation matrices and requires the application of correction methods to maintain statistical integrity.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingCorrelation