Data Scientist Interview Questions

Showing 601–630 of 1076 results

• MetaMedium·Data Scientist
  Design 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
• MetaHard·Data Scientist
  Design 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
• MetaMedium·Data Scientist
  Design 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
• MetaMedium·Data Scientist
  Design 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
• MetaMedium·Data Scientist
  Design 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
• MetaEasy·Data Scientist
  Detect 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
• MetaMedium·Data Scientist
  Determine 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
• 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
• MetaMedium·Data Scientist
  Employ 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
• MetaMedium·Data Scientist
  Estimate 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
• MetaHard·Data Scientist
  Estimate 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
• 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
• 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 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
• 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
• 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
• 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
  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
• MetaMedium·Data Scientist
  Handle sales pressure with analytical integrity

  This question evaluates a data scientist's skills in analytical integrity, causal inference, experimental design, stakeholder communication, and change management when pressured to convert a correlation into policy.

  Behavioral & LeadershipTechnical Screen
• 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
• MetaHard·Data Scientist
  How to design Shop ad ranking

  This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.

  Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems
• 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