Meta interview questions

Showing 331–360 of 666 results

• Estimate Lift and Significance in Facebook Ad CampaignsMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Estimate Portal’s causal lift on video-call usageMedium

  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.

  MetaData ScientistStatistics & MathTechnical Screen
• Estimate and validate weights for engagement actionsHard

  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.

  MetaData ScientistStatistics & MathTechnical Screen
• Estimate bots and CI from DAU spikeMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Estimate fake-account prevalence with capture-recaptureMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Estimate revenue of organic shopping tabHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Estimate variance for ratio metricsHard

  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.

  MetaData ScientistStatistics & MathOnsite
• Evaluate AI-assisted ad creationMedium

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate AI-assisted ads creation featureEasy

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate Classifier with Precision, Recall, and Fairness MetricsMedium

  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.

  MetaData ScientistMachine LearningTechnical Screen
• Evaluate Facebook Dating launch and validate successHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Factors Before Replacing Recommendation ModelHard

  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.

  MetaData ScientistMachine LearningOnsite
• Evaluate Fake-Account Classifier with Precision and Recall MetricsMedium

  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.

  MetaData ScientistMachine LearningOnsite
• Evaluate Impact of Increasing Stranger Content in FeedsMedium

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Instagram's Short-Video Recommender System SuccessMedium

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate Marketing Campaign's Click-Through Rate EffectivenessMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Evaluate Messenger's P2P Payments Feature for Business ViabilityHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate New Ad Model with A/B Testing ExperimentHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate Notification-Based Account RankingMedium

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Probability of Positive User Comments and Model PerformanceMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Evaluate a Live-Stream Group Notification Under Network EffectsMedium

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate a New Ads-Ranking AlgorithmMedium

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate arithmetic expression without parenthesesMedium

  Evaluate arithmetic expression without parentheses evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Evaluate expression without stack, constant spaceMedium

  Evaluate expression without stack, constant space evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Evaluate fake accounts and ad creationMedium

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluate impact of short videos in feedHard

  This question evaluates competency in metrics definition, causal experimentation, product analytics, and feed system behavior within the System Design and Data Engineering domain.

  MetaData EngineerSystem DesignOnsite
• Evaluate shopping tab pre- and post-launchHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate the Health of Facebook GroupsHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Evaluating a 15 % reduction in post‑card heightMedium

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluating the Facebook ‘Memory’ featureMedium

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen