Data Scientist Interview Questions

Showing 181–210 of 344 results

• MetaMedium·Data Scientist
  Compute fraud probabilities with Bayes and Binomial

  This question evaluates a candidate's understanding of probabilistic modeling and statistical decision-making, focusing on the Binomial distribution for session-level events and Bayes' theorem for posterior probabilities in a fraud-detection setting.

  Statistics & MathOnsite
• MetaHard·Data Scientist
  Compute p-values, power, and adjust errors

  This question evaluates statistical reasoning and applied inference skills including hypothesis testing for proportions with sequential monitoring, multiple-testing control (Benjamini–Hochberg vs Bonferroni), Bayesian posterior estimation for classification flags, and robust AOV estimation and transformation techniques.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compute posterior and event counts in fraud screen

  This question evaluates a candidate's competence in probability and statistical inference, covering binomial probability calculations, Bayes' theorem for posterior probability, rare-event base-rate reasoning, and modeling of correlated binary signals in a fraud-screening context.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compute posterior fake probability using Bayes' rule

  The problem evaluates understanding of Bayes' rule and conditional probability within the Statistics & Math domain for a Data Scientist role, at an introductory applied probability/interpretation level.

  Statistics & MathTechnical Screen
• MetaEasy·Data Scientist
  Compute probabilities for chatbot response quality

  This question evaluates a candidate's grasp of basic probability concepts, including independence, joint event probabilities, and i.i.d. assumptions. It is commonly asked to test probabilistic reasoning about repeated trials within the Statistics & Math domain and is primarily a conceptual understanding problem with a simple practical application.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Compute probability an account is fake

  This question tests the ability to apply Bayes' Theorem to update the probability of a hypothesis (an account being fake) given new evidence (an automated flag).

  Statistics & MathTechnical ScreenBayesian StatisticsConditional ProbabilityClassification Metrics
• MetaHard·Data Scientist
  Compute sample size and test duration correctly

  This question evaluates competency in experiment design and statistical power calculations, including sample size estimation, variance misspecification effects, multiple-comparison corrections, sequential monitoring boundaries, and adjustments for covariates or clustering.

  Statistics & MathTechnical Screen
• MetaMedium·Data Scientist
  Construct a 95% Confidence Interval for Comment Counts

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Design Sampling Strategy to Estimate Fake News Proportion

  Design Sampling Strategy to Estimate Fake News Proportion 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
  Determine Posterior Probability of Bad User Prediction

  Meta Bayes theorem question on a bad-actor classifier, covering prevalence, sensitivity, specificity, false positives, positive predictive value, and the base-rate effect.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Determine Probability of Fourth Good Response After Three Successes

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Probability of Fourth Good Response After Three Successes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Determine Probability of Friend Request Being Fake

  Determine Probability of Friend Request Being Fake 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
• MetaHard·Data Scientist
  Estimate CTR lift with binomial tests and errors

  This question evaluates statistical inference for proportions, sequential monitoring, and multiple-comparison error control—assessing competence with binomial-based CTR comparison, pooled standard errors, z-tests and confidence intervals, Type I error inflation from peeking, and FWER/FDR considerations.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Estimate Lift and Significance in Facebook Ad Campaigns

  Meta statistics prompt on Facebook ad conversion lift, covering absolute and relative lift, two-proportion tests, confidence intervals, power for 5% relative lift, and Bayesian beta-binomial reframing.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Estimate Portal’s causal lift on video-call usage

  This problem tests the ability to design an observational causal inference framework using Difference-in-Differences (DiD) in a setting with staggered adoption, specifically addressing selection bias and household-level measurement errors.

  Statistics & MathTechnical ScreenCausal InferenceDifference-in-DifferencesStaggered Adoption
• MetaHard·Data Scientist
  Estimate and validate weights for engagement actions

  This question tests the ability to derive an engagement scoring model, handle high-dimensional collinear features, and establish a robust validation framework for point-in-time predictions.

  Statistics & MathTechnical ScreenRegressionRegularizationMulticollinearity
• MetaMedium·Data Scientist
  Estimate bots and CI from DAU spike

  This question tests the ability to decompose a mixture of legitimate user traffic and synthetic bot/CI traffic using statistical modeling, and requires justifying the choice of distribution for overdispersed count data.

  Statistics & MathOnsiteMixture ModelsNegative BinomialBootstrapping
• MetaMedium·Data Scientist
  Estimate fake-account prevalence with capture-recapture

  This question tests the ability to apply the Lincoln-Petersen estimator to estimate a hidden population size and addresses the limitations of the assumptions underlying capture-recapture methods.

  Statistics & MathOnsiteStatisticsEstimationInference
• MetaHard·Data Scientist
  Estimate variance for ratio metrics

  This question evaluates statistical inference and experimental-analysis skills for data scientists, including variance estimation for ratio metrics via the delta method, covariance estimation from sample moments, model-based comparisons (ANCOVA/log models with offsets), bootstrap and t-test inference considerations, and robustness techniques for heavy-tailed revenue such as winsorization and robust M-estimators. It is commonly asked to assess both theoretical derivation and practical estimation trade-offs in A/B or experimental settings; the category is Statistics & Math and the level of abstraction spans conceptual understanding and practical application.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Evaluate Marketing Campaign's Click-Through Rate Effectiveness

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Evaluate Marketing Campaign's Click-Through Rate Effectiveness states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Evaluate Probability of Positive User Comments and Model Performance

  Evaluate Probability of Positive User Comments and Model Performance 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
  Explain Central Limit Theorem's Importance in A/B Testing

  Evaluates understanding of the Central Limit Theorem and confidence intervals in A/B testing. Strong answers explain sampling distributions, standard error, normal approximations for experiment metrics, caveats for dependence and heavy tails, and compute the 95% confidence interval as [2.902, 3.098].

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Fake Accounts [AE]

  This question tests your ability to apply Bayes' Theorem in a low-prevalence classification scenario and your ability to engineer robust signals for detecting anomalous user behavior.

  Statistics & MathOnsiteBayes TheoremClassificationPrecision-Recall
• MetaMedium·Data Scientist
  Identify Metrics to Detect Fake-Account Activity on Facebook

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Model Unique Recipients with Poisson Distribution and Test Fit

  Evaluates statistical modeling of unique recipient counts per caller using count distributions. Strong answers compare Poisson, binomial, negative binomial, zero-inflated, and segmented models, then test fit with dispersion checks, likelihood metrics, chi-square tests, and simulation-based diagnostics.

  Statistics & MathTechnical Screen
• MetaMedium·Data Scientist
  Model session times and comments with exponential/Poisson

  This question evaluates understanding of memoryless processes and count-data modeling, covering exponential (constant-hazard) distributions for session durations and Poisson approximations for comment counts within survival analysis and count-data modeling in Statistics & Math for a Data Scientist role.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Test two models' proportions for significance

  This question evaluates proficiency in statistical inference for proportions, covering hypothesis testing, confidence intervals, power and sample-size calculations, multiple-testing correction, and comparison of frequentist versus Bayesian approaches within the Statistics & Math domain for data scientist roles.

  Statistics & MathOnsite
• MicrosoftMedium·Data Scientist
  Choose Classification Metrics Under Asymmetric Costs

  This question evaluates understanding of binary classification evaluation metrics and model-operating decisions, covering confusion-matrix-derived measures (precision, recall/sensitivity, specificity, false positive/negative rates), summary metrics (ROC-AUC, PR-AUC), Type I/II errors, threshold selection, prevalence drift, probability calibration, and cost-sensitive trade-offs. It is commonly asked to assess a data scientist's ability to reason about model performance under class imbalance and asymmetric business costs, and it belongs to the Statistics & Math / Machine Learning model-evaluation domain requiring both conceptual understanding and practical application.

  Statistics & MathTechnical Screen
• MicrosoftEasy·Data Scientist
  Compute P(Bag B | red) via Bayes

  This question evaluates understanding of Bayes' rule and conditional probability by requiring computation of the posterior probability that a particular bag was chosen given a red draw.

  Statistics & MathTechnical Screen
• MicrosoftMedium·Data Scientist
  Test classifier difference with McNemar's test

  This question tests a data scientist's ability to apply McNemar's test for comparing paired classifiers, a core competency in statistical hypothesis testing and model evaluation. It assesses practical knowledge of contingency table analysis, exact vs. asymptotic tests, confidence interval construction, and multiple testing correction in machine learning contexts.

  Statistics & MathOnsite