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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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Showing 331–360 of 641 results
- MetaEasyData ScientistCompute probabilities for chatbot response quality
This question evaluates a candidate's grasp of basic probability concepts, including independence, joint event probabilities, and i.i.d. assumptions. It is commonly asked to test probabilistic reasoning about repeated trials within the Statistics & Math domain and is primarily a conceptual understanding problem with a simple practical application.
Statistics & MathOnsite - MetaEasyData ScientistCompute this-year spend share of last-year whales
This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.
Data Manipulation (SQL/Python)OnsiteSQLAggregationCohort Analysis - MetaMediumData ScientistCompute time-spent percentage by app category
This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistConstruct a 95% Confidence Interval for Comment Counts
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaHardData ScientistDecide when CTR falls but revenue rises
This question evaluates understanding of A/B experimentation, auction-based ad metrics, metric decomposition for revenue impact, causal diagnostics for hidden heterogeneity (e.g., Simpson’s paradox), and executive-level results presentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDefine Product Metrics: Align Stakeholders, Measure Success, Improve Results
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Define Product Metrics: Align Stakeholders, Measure Success, Improve Results states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumData ScientistDefine and compute shop visibility in SQL
This question evaluates the ability to define and compute shop-level visibility KPIs in SQL, testing skills in metric design (visibility_rate and impression_share), deduplication, session/window filtering, and data-quality concerns such as late-arriving events and bot filtering.
Data Manipulation (SQL/Python)Onsite - MetaEasyData ScientistDefine engagement metrics and analyze comment distribution
This question evaluates a data scientist's competency in metric framework design, distributional analysis of heavy-tailed user behavior, monitoring and anomaly detection, and experimental setup for measuring healthy engagement, including considerations like unit of analysis, treatment of heavy users, and anti-spam guardrails.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDemonstrate leadership in cross-functional collaboration
The Meta Data Scientist onsite behavioral & leadership round: answer a set of STAR-format leadership prompts spanning cross-functional collaboration and conflict, giving/receiving feedback, disagree-and-commit, being wrong and course-correcting, overcoming obstacles and winning skeptics, driving breakthroughs, ambiguous work under time pressure, breaking-metric triage, learning from peers, diversity & inclusion, inclusive meetings, proactive alignment, and building long-term trust. Each answer must name stakeholders, state trade-offs, and quantify impact against a baseline.
Behavioral & LeadershipOnsite - MetaHardData ScientistDeploy multi-armed bandits safely
This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.
Machine LearningOnsite - MetaMediumData ScientistDescribe 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 - MetaEasyData ScientistDescribe 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 - MetaMediumData ScientistDescribe 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 - MetaMediumData ScientistDescribe 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 - MetaEasyData ScientistDescribe 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 - MetaHardData ScientistDesign 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 - MetaMediumData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaMediumData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaMediumData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaMediumData ScientistDesign 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 - MetaHardData ScientistDesign 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 - MetaHardData ScientistDesign 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
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