Data Scientist Interview Questions

Showing 271–300 of 513 results

• MetaHard·Data Scientist
  Design Experiment to Measure Shopping Feature Impact

  Meta data scientist experimentation prompt on measuring Instagram Shopping impact with A/B tests, geo or cluster holdouts, engagement and revenue metrics, guardrails, selection bias, network effects, and rollout decisions.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Design Messenger spam experiment with clustering

  This question evaluates a data scientist's skills in experimental design, causal inference, clustering and interference analysis, metric specification, and power/sample-size calculation within the Analytics & Experimentation domain for Data Scientist roles.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Design Metrics to Track and Analyze Spam Impact

  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 Design Metrics to Track and Analyze Spam Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design a clustered A/B test with spillovers

  This question tests the ability to design a cluster-randomized experiment when interactions between users (network interference) violate the stable unit treatment value assumption (SUTVA).

  Analytics & ExperimentationHR ScreenCausal InferenceExperimental DesignStatistics
• MetaMedium·Data Scientist
  Design a clustered notification experiment with guardrails

  This question evaluates experimental-design and causal-inference competencies for cluster-randomized experiments, covering metric selection, power and design-effect calculations, intracluster correlation, spillover detection, sequential monitoring, guardrails, and operational rollout within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to determine how applicants balance statistical rigor and real-world operational constraints—such as cluster choice, monitoring thresholds, and analysis strategy—testing both conceptual understanding and practical application in production experimentation.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design a feed ads A/B test with guardrails

  This question evaluates a data scientist's competency in experimental design, causal inference, statistical power analysis, and product-metric trade-offs within the Analytics & Experimentation domain, and is commonly asked to assess the ability to increase monetization while protecting user and creator experience.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Design a small-sample launch experiment in Europe

  This question evaluates a data scientist's competencies in experimental design, causal inference, estimand specification, power and MDE calculations for heavy-tailed and overdispersed outcomes, interference handling, compliance/IV considerations, and sequential monitoring in field experiments.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Design an A/B Test for Group Video Calls Impact

  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 Design an A/B Test for Group Video Calls Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design an A/B test for pinned-unread feature

  This question evaluates proficiency in experiment design and causal inference for product analytics, including choosing the randomization unit, defining eligibility and exposure, selecting primary and guardrail metrics, performing power/MDE and sample-size analysis, and addressing identity cross-contamination and network effects.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design an Experiment to Evaluate New ML Model

  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 Design an Experiment to Evaluate New ML Model states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Design an Experiment to Evaluate New Recommendation Model

  This problem tests the ability to design an end-to-end A/B test framework for a machine learning model update, focusing on metric selection, causal validity, and post-experiment analysis.

  Analytics & ExperimentationOnsiteA/B TestingExperimentationMetric Design
• MetaHard·Data Scientist
  Design an experiment to evaluate a new ads algorithm

  This question evaluates a data scientist's competency in experimental design, causal inference, metric selection and trade-offs, randomization and interference reasoning, rollout strategies, and observational methods for algorithm evaluation, including consideration of stakeholder impacts such as users, advertisers, and revenue.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design analytics and experiment for group video calls

  This question evaluates product analytics and experimentation competencies, including tie-strength and group-affinity measurement, event-level instrumentation design, proxy-product forecasting, participant-cap constraints and operational trade-offs, and experiment design that accounts for network effects and contamination.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Design and analyze a group-calls experiment

  This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design and evaluate P2P payments in messaging

  This question evaluates a data scientist's competency in product analytics, causal inference, experimental design under network interference, monetization measurement (ARPU impact), and risk mitigation for a peer-to-peer payments feature relevant to a Data Scientist role.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Design and validate ad model launch

  This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Design and validate an ads feed experiment

  This question tests the ability to design a rigorous A/B test for a core product feature, covering metric selection, interference handling, and post-experiment analysis under complex constraints.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceProduct Metrics
• MetaHard·Data Scientist
  Design cluster-randomized test under network effects

  This question evaluates a data scientist's experiment design and causal inference skills under interference, covering exposure and estimand definition, graph-based cluster construction, cluster-level randomization and contamination mitigation, metric selection, and design-effect/sample-size calculations.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design 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
• MetaHard·Data Scientist
  Design 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
• 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
• MetaEasy·Data Scientist
  Design 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
• 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 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
• MetaMedium·Data Scientist
  Detect 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
• 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
• MetaHard·Data Scientist
  Determine 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
• MetaMedium·Data Scientist
  Determine 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