Data Scientist Interview Questions

Showing 301–330 of 513 results

• MetaMedium·Data Scientist
  Determine 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
• MetaHard·Data Scientist
  Determining 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
• MetaHard·Data Scientist
  Diagnose 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
• 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
• 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
• 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
• 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
• 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 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 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
  How investigate a brand-ad spend drop?

  This question evaluates a data scientist's competency in diagnosing ad-spend anomalies, including data validation, attribution and reporting integrity, segmentation and placement effects, and causal inference within advertising analytics.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  How to Validate Friends' Content Engagement Hypothesis?

  Evaluates how to validate whether friends' content drives stronger social engagement than unconnected content. Strong answers define socialness metrics, control observational bias, test launch impact for unconnected feed content, and monitor discovery, retention, cannibalization, and quality guardrails.

  Analytics & ExperimentationTechnical Screen
• MetaEasy·Data Scientist
  How to evaluate a similar-listing notifications feature

  A Meta Data Scientist analytics-and-experimentation interview question: a US C2C second-hand marketplace is considering opt-in “similar listings” notifications. You must (1) decide whether to build it — hypotheses, opportunity sizing, risks, MVP plan — and (2) design the post-launch measurement: a user-randomized A/B test with a primary/diagnostic/guardrail metric stack, handling of opt-in selection bias, and a clear launch/rollback rule.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  How to evaluate new listing notifications?

  This problem tests your ability to design an A/B test for a marketplace notification feature, focusing on balancing buyer engagement with potential cannibalization and ecosystem health metrics.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace
• MetaEasy·Data Scientist
  How would you define and use retention metrics?

  This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.

  Analytics & ExperimentationTechnical Screen
• MetaEasy·Data Scientist
  How would you evaluate emoji reactions launch?

  This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.

  Analytics & ExperimentationOnsiteProduct MetricsA/B TestingCausal Inference
• MetaEasy·Data Scientist
  How would you evaluate pixel-issue notifications?

  This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.

  Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference
• MetaEasy·Data Scientist
  How would you evaluate upranking shop ads?

  This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech
• MetaMedium·Data Scientist
  How would you measure Group Call success?

  This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.

  Analytics & ExperimentationTechnical Screen