Data Scientist Interview Questions

Showing 1111–1140 of 1971 results

• MetaHard·Data Scientist
  Estimate revenue of organic shopping tab

  This question evaluates a data scientist's skills in revenue modeling, funnel construction, causal inference for bias correction, experimentation scaling, and sensitivity analysis using organic engagement and purchase metrics.

  Analytics & ExperimentationOnsite
• 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 AI-assisted ad creation

  This question evaluates competency in product analytics, experimentation design, causal inference, metric definition, and measurement of downstream business and user-quality impacts for an AI-assisted advertising feature.

  Analytics & ExperimentationOnsite
• MetaEasy·Data Scientist
  Evaluate AI-assisted ads creation feature

  This question assesses the ability to design an A/B test for a new AI feature, focusing on metric definition, experiment design, and mitigating potential trade-offs between advertiser efficiency and ad quality.

  Analytics & ExperimentationOnsiteA/B TestingProduct MetricsExperiment Design
• MetaMedium·Data Scientist
  Evaluate Classifier with Precision, Recall, and Fairness Metrics

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Data Scientist
  Evaluate Facebook Dating launch and validate success

  This question evaluates a data scientist's competency in product analytics, experimental design, causal inference, and launch validation by requiring definition of primary and guardrail metrics, test unit and spillover considerations, power/duration planning, behavioral signal expectations, quasi-experimental alternatives, and scale/readiness criteria. Commonly asked in Analytics & Experimentation interviews because organizations need assurance a pilot can be scaled safely and reliably, it tests practical application of experimental methods alongside conceptual understanding of statistical power, spillover risk, diagnostic checks, and operational concerns such as privacy and abuse monitoring.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Evaluate Factors Before Replacing Recommendation Model

  Evaluates whether an ads recommendation model should replace an existing production model. Strong answers combine offline validation, online experiments, revenue and advertiser outcomes, user guardrails, conflicting metric interpretation, rollout safety, rollback planning, and executive communication.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Evaluate Fake-Account Classifier with Precision and Recall Metrics

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate Fake-Account Classifier with Precision and Recall Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Evaluate Impact of Increasing Stranger Content in Feeds

  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 Evaluate Impact of Increasing Stranger Content in Feeds states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Evaluate Instagram's Short-Video Recommender System Success

  Evaluates success metrics and experimentation for Instagram's short-video recommender feed. Strong answers choose a durable user-value metric, understand heavy-tailed engagement distributions, handle metric trade-offs, and outline a safe A/B test with retention, safety, creator, and app-level guardrails.

  Analytics & ExperimentationOnsite
• 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
• MetaHard·Data Scientist
  Evaluate Messenger's P2P Payments Feature for Business Viability

  Evaluates business viability for a Messenger P2P payments feature. Strong answers define networked adoption and transfer metrics, assess fraud, compliance, support, trust, and unit economics, and propose a beta or experiment design that handles spillovers and contamination.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Evaluate New Ad Model with A/B Testing Experiment

  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 Evaluate New Ad Model with A/B Testing Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Evaluate Notification-Based Account Ranking

  This question tests the ability to design an experiment to measure the causal impact of a new notification strategy on account ranking quality and user engagement.

  Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMetrics
• 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
• MetaMedium·Data Scientist
  Evaluate a Live-Stream Group Notification Under Network Effects

  This question tests the ability to design an experiment that accounts for spillover effects and user fatigue in a social graph, while proposing causal inference methods for environments where randomization is restricted.

  Analytics & ExperimentationTechnical ScreenCausal InferenceNetwork EffectsExperimentation
• MetaMedium·Data Scientist
  Evaluate a New Ads-Ranking Algorithm

  This question tests the ability to design a robust A/B test for an ads-ranking system while accounting for interference effects, budget constraints, and conflicting metric signals.

  Analytics & ExperimentationOnsiteA/B TestingAd-TechMetrics
• MetaMedium·Data Scientist
  Evaluate fake accounts and ad creation

  This question tests the ability to design an evaluation framework for identifying malicious actors in an ad-tech environment and measuring the performance of automated content generation tools.

  Analytics & ExperimentationOnsiteAnalyticsExperimentationMetric Design
• MetaHard·Data Scientist
  Evaluate shopping tab pre- and post-launch

  This question evaluates a data scientist's competency in causal inference, experiment design, funnel metric instrumentation, revenue modeling, sensitivity analysis, and data‑quality triage within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Evaluate the Health of Facebook Groups

  Evaluates Facebook Group health metrics and threaded-comments experiment design. Strong answers define normalized health scores, compare small and large groups fairly, and test deep interaction with safety guardrails.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Evaluating a 15 % reduction in post‑card height

  Evaluates feed UX experimentation for reducing post-card height and diagnosing divergent geographic revenue outcomes. Strong answers measure scroll efficiency, viewability, engagement, ad revenue, device/network effects, and geo-specific decisions.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Evaluating the Facebook ‘Memory’ feature

  Evaluates Facebook Memories feature performance beyond click-through rate and surface-level engagement. Strong answers measure incremental engagement, retention, cannibalization, quality guardrails, frequency caps, sensitive-content handling, and launch criteria.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Evaluating the Impact of Duplicate and Stolen Posts on a Content Platform

  This question tests the ability to design a measurement framework for evaluating content-quality problems on a large user-generated-content platform. It assesses competency in metric selection, experimentation design, and causal reasoning within the Analytics & Experimentation domain — skills central to data science roles focused on platform health.

  Analytics & ExperimentationTechnical Screen
• 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
  Explain Your Motivation for Career Transition and Role Interest

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Your Motivation for Career Transition and Role Interest states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• MetaEasy·Data Scientist
  Explain why IG Story usage exceeds Facebook

  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 Explain why IG Story usage exceeds Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• 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
  Find least active countries

  This question tests the ability to perform multi-step data aggregation using SQL, specifically focusing on joining, filtering by date ranges, calculating distinct counts, and identifying bottom-tier performers across different time periods.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationJoins
• MetaMedium·Data Scientist
  Find the most-used app

  This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.

  Data Manipulation (SQL/Python)Onsite
• MetaEasy·Data Scientist
  Handle feedback, change pivots, and conflict

  A Meta Data Scientist technical-screen behavioral question covering three leadership prompts: giving or receiving constructive feedback, pivoting when a project's direction or business conditions change, and resolving conflict with a teammate or stakeholder. The model answer gives a STAR rubric, strong vs. weak signals, and a practical template for each story.

  Behavioral & LeadershipTechnical Screen