Data Scientist Interview Questions

Showing 1051–1080 of 1971 results

• MetaMedium·Data Scientist
  Describe Overcoming Ambiguity and Building Cross-Team Collaboration

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Overcoming Ambiguity and Building Cross-Team Collaboration states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• MetaEasy·Data Scientist
  Describe a challenging project and work-style conflicts

  Meta Data Scientist onsite behavioral & leadership round, anchored on describing a challenging project end-to-end with follow-up prompts on stakeholder influence, collaborating across work styles, mentoring and onboarding teammates, managing cross-functional relationships under disagreement, learning quickly, and quantifying impact and trade-offs. Includes a STAR-based rubric of what interviewers look for at each prompt, common pitfalls, and senior/IC6 expectations.

  Behavioral & LeadershipOnsite
• MetaMedium·Data Scientist
  Describe influencing without authority

  This question evaluates a candidate's ability to influence without formal authority, covering stakeholder management, cross-functional communication, conflict resolution, and technical trade-off quantification within a Behavioral & Leadership interview for a Data Scientist role.

  Behavioral & LeadershipOnsite
• MetaMedium·Data Scientist
  Describe leadership and collaboration examples

  The question evaluates a Data Scientist's leadership, cross-functional collaboration, stakeholder communication, conflict resolution, onboarding, and impact-measurement competencies within a product analytics context.

  Behavioral & LeadershipOnsite
• MetaEasy·Data Scientist
  Describe something you did to be inclusive

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe something you did to be inclusive states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Design A/B test and success metrics for new feature

  This question evaluates a data scientist's competency in experimental design, metric selection and guardrails, power and sample-size calculations, logging and attribution specifications, and revenue impact estimation for a new feature on a social media platform.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Design B2C chatbot success metrics and test plan

  This question evaluates a data scientist's ability to design product success metrics and a rigorous experiment plan, covering KPI formulation, guardrail metrics and thresholds, randomization and experiment design, logging instrumentation, and quasi-experimental causal inference.

  Analytics & ExperimentationOnsite
• 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 Machine Learning Model for Facebook Groups Post Ranking

  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 Design Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• 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
• MetaMedium·Data Scientist
  Design SQL Query for Shop Visibility and User Activity Metrics

  This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Design Sampling Strategy to Estimate Fake News Proportion

  Design Sampling Strategy to Estimate Fake News Proportion 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
• MetaHard·Data Scientist
  Design a Restaurant Recommendation System for Food Apps

  Meta machine learning system-design prompt on restaurant recommendations for food apps, covering retrieval and ranking models, features, cold start, implicit feedback, offline metrics, A/B testing, and marketplace guardrails.

  Machine LearningOnsite
• 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
• MetaMedium·Data Scientist
  Design an ad recommendation and ranking system

  This question evaluates end-to-end ad recommendation and ranking design skills, covering machine learning modeling, recommender systems, causal and counterfactual evaluation, online learning, and production ML engineering within the Machine Learning domain, at a high-level system-design and modeling abstraction.

  Machine LearningOnsite
• 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