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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 1081–1110 of 1971 results
- MetaHardData ScientistDesign experiment for Group Calls with interference
This question evaluates a data scientist's competency in experimental design and causal inference for networked features, covering metrics selection (primary and guardrail metrics), strategies for mitigating interference via randomization or quasi-experimental approaches, exposure and contamination controls, rollout planning, and pre-registration and power analysis. Commonly asked in Analytics & Experimentation interviews for Data Scientist roles, it assesses reasoning about bias–variance trade-offs, engineering constraints, and validity diagnostics in interconnected user environments, requiring both conceptual understanding of identification and practical application of experiment implementation.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign experiments under network interference
This question evaluates a data scientist's competence in experimental design and causal inference under network interference, assessing understanding of clustering strategies, exposure and exclusion definitions, contamination control, design effect and sample-size implications, estimation of intra-cluster correlation, and mixed-effects analysis for evolving clusters. Commonly asked in Analytics & Experimentation interviews to probe the ability to preserve internal validity while managing operational feasibility in two-sided marketplaces, it tests both conceptual understanding of interference and practical application skills in design and analysis planning.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDesign marketplace experiments at DoorDash
This question evaluates a candidate's skills in experimental design, product and marketplace analytics, causal inference, and diagnostic data science for multi-sided platforms.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign metrics for violating content exposure
This question evaluates competency in metric design and measurement for content safety, including defining exposure metrics, inclusion/exclusion rules, handling label latency and appeals, uncertainty-aware alerting, and A/B test design for a Data Scientist role in the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistDesign metrics to detect harmful content and fraud
This problem tests the ability to design robust monitoring metrics for detecting fraudulent user activity and policy-violating content, focusing on trade-offs between precision and recall.
Analytics & ExperimentationOnsiteAnalyticsMetricsFraud-Detection - MetaMediumData ScientistDesign pre-launch plan and cluster A/B test
This question evaluates a candidate's skills in experimental design, causal inference, cluster randomization, metric definition and instrumentation, power analysis, and phased rollout decision-making.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDesign robust group size limiting for calls
This question evaluates distributed systems design, concurrency control, admission-control algorithms, and scalability under real-time and race-condition constraints.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistDesign video-ads experiment and handle null results
This question evaluates experimental design, causal inference, statistical power analysis, metric definition, and operational analytics competency within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDetect and address Simpson’s paradox
This question evaluates understanding of aggregation bias and Simpson's paradox, proficiency with stratified or mixed-model estimators, heterogeneity testing, and experiment decisioning within Analytics & Experimentation for a Data Scientist role.
Analytics & ExperimentationOnsite - MetaEasyData ScientistDetect bots using comment distribution patterns
This problem tests your ability to define metrics for identifying automated behavior and your statistical intuition for validating those metrics against human behavior.
Analytics & ExperimentationTechnical ScreenAnalyticsAnomaly DetectionFeature Engineering - MetaHardData ScientistDetect leakage and evaluate a prediction model
This question evaluates competence in production-ready supervised machine learning—detecting and repairing data leakage in logs-based features, designing time-aware cross-validation, defining KPI-driven thresholding and calibration, interpreting model coefficients, and establishing monitoring and retraining policies for a churn prediction model in the Machine Learning domain. It is commonly asked because interviewers need to assess both conceptual understanding of leakage, validation, and calibration principles and practical application skills for deploying and maintaining models with cost-sensitive objectives and monitoring in production, so the level of abstraction spans conceptual reasoning and hands-on practical application.
Machine LearningOnsite - MetaMediumData ScientistDetermine High-Quality Notifications with CTR Analysis
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 Determine High-Quality Notifications with CTR Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDetermine Key Metrics and Design A/B Test for Ad Ranking
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 Determine Key Metrics and Design A/B Test for Ad Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDetermine Metrics to Evaluate Notification Impact on Users
Evaluates product analytics judgment for Facebook push notifications, including how to measure helpful versus harmful notifications, design an event-notification experiment, and balance short-term clicks against retention, fatigue, and opt-out guardrails. Strong answers build a metric hierarchy and use causal measurement.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDetermine Old vs. New Users' Shop Visibility Changes
This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.
Data Manipulation (SQL/Python)Onsite - MetaEasyData ScientistDetermine 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 - MetaMediumData ScientistDetermine 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 - MetaEasyData ScientistDetermine 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 - MetaMediumData ScientistDetermine Unhealthy Oculus Usage with SQL Analysis
This question evaluates SQL-based data manipulation and time-series analysis competencies, including sessionization, user-day aggregation, and detection of sustained behavioral patterns in event data; it is categorized under Data Manipulation (SQL/Python) and targets practical application skills.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistDetermine Value of Prioritizing Accounts by Unread Notifications
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 Determine Value of Prioritizing Accounts by Unread Notifications states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDetermining the optimal ad load in News Feed
Evaluates optimal News Feed ad-load strategy using marginal revenue, user tolerance, and long-term LTV. Strong answers test ad-load levels, segment tolerance, personalize caps, and monitor revenue, retention, and user-experience guardrails.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDiagnose sales correlations without claiming causality
This question tests the ability to design an observational study that identifies associations between a new feature adoption and sales growth while strictly avoiding causal claims in the presence of self-selection bias.
Analytics & ExperimentationTechnical ScreenObservational StudyCausal InferenceAnalytics - MetaMediumData ScientistEmploy Collaborative Filtering for Personalized Recommendation Lists
Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.
Machine LearningTechnical Screen - MetaMediumData ScientistEnsure Effective Teamwork Amid Conflicting Stakeholder Opinions
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Ensure Effective Teamwork Amid Conflicting Stakeholder Opinions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaHardData ScientistEstimate 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 - MetaMediumData ScientistEstimate 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 - MetaMediumData ScientistEstimate 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 - MetaHardData ScientistEstimate 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 - MetaMediumData ScientistEstimate 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 - MetaMediumData ScientistEstimate 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
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