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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
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- 11
- Topics
- 8
- Languages
Showing 301–330 of 641 results
- MetaMediumData ScientistAnalyze 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 - MetaMediumData ScientistAnnotating 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 - MetaMediumData ScientistApply reinforcement learning to product decisions
This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.
Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation - MetaHardData ScientistApply 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 - MetaMediumData ScientistAssess Cultural Fit and Problem-Solving in Reality Labs Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit and Problem-Solving in Reality Labs Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaHardData ScientistBuild dashboard; diagnose engagement–purchase gap
This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCalculate 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 - MetaMediumData ScientistCalculate Ad Performance with Click-Through and Conversion Rates
This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.
Data Manipulation (SQL/Python)Onsite - MetaEasyData ScientistCalculate 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 - MetaEasyData ScientistCalculate 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 - MetaEasyData ScientistCalculate 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 - MetaEasyData ScientistCalculate 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 - MetaMediumData ScientistCalculate Total Revenue in USD Using SQL Query
This question evaluates proficiency in SQL data manipulation, focusing on joins, aggregations, and handling currency conversion across relational tables.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCalculate and Compare Survey Response Rates for User Tenure
This question evaluates proficiency in relational data manipulation and metric computation, including SQL joins, aggregation and cohort-based comparisons relevant to survey response analysis, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCharacterize 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 - MetaMediumData ScientistCharacterize 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 - MetaHardData ScientistChoose alternatives when randomization fails
This question evaluates proficiency in causal inference and experimentation analytics for a Data Scientist role, focusing on selecting appropriate identification strategies, designing placebo and pre‑trend tests, and specifying robustness and sensitivity analyses for non‑randomized feature adoption.
Analytics & ExperimentationOnsite - MetaMediumData ScientistChoose threshold under asymmetric costs
This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.
Machine LearningOnsite - MetaEasyData ScientistCompare 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 - MetaMediumData ScientistCompare 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 - MetaMediumData ScientistCompare Instagram and Facebook Stories Using Key Performance Metrics
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCompare Instagram vs. Facebook using causal experiments
This question evaluates causal inference, experimentation design, metric engineering, and ecosystem-level impact assessment skills for a Data Scientist in the Analytics & Experimentation domain, with emphasis on defining a single-objective OEC that prevents pure cannibalization and on measuring cross-app effects via randomized rollouts.
Analytics & ExperimentationOnsite - MetaEasyData ScientistCompute 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 - MetaMediumData ScientistCompute Each Advertiser's Share of Shop Ad Spend
This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.
Data Manipulation (SQL/Python)OnsiteSQLAggregationWindow Functions - MetaMediumData ScientistCompute Shop Visibility Rate Using SQL and Python
This question evaluates proficiency in data manipulation and metric design using SQL and Python, focusing on event-level aggregation, user deduplication, and definition of a business visibility metric within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - MetaEasyData ScientistCompute active ad revenue by creation source
This question evaluates proficiency in data manipulation and analytics, testing skills such as joining event and impression tables, aggregating time-series revenue, and counting distinct active ads broken down by creation source.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCompute ad impression conversion rates
This question evaluates proficiency in time-based event attribution, aggregation of business metrics (impressions, conversion rates, spend and revenue), timestamp handling, and user/ad-level matching within the Data Manipulation (SQL/Python) domain, emphasizing practical application over abstract theory.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCompute 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 - MetaHardData ScientistCompute 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 - MetaMediumData ScientistCompute 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
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