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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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- Questions
- 546
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- 11
- Topics
- 8
- Languages
Showing 1141–1170 of 1971 results
- MetaMediumData ScientistHandle 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 - MetaMediumData ScientistHow 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 - MetaMediumData ScientistHow 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 - MetaHardData ScientistHow 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 - MetaEasyData ScientistHow 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 - MetaMediumData ScientistHow 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 - MetaEasyData ScientistHow would you define and use retention metrics?
This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistHow would you design a Shop Ads ranking algorithm?
This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.
Machine LearningTechnical ScreenSystem DesignRankingAds - MetaEasyData ScientistHow would you evaluate emoji reactions launch?
This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.
Analytics & ExperimentationOnsiteProduct MetricsA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate pixel-issue notifications?
This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.
Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate upranking shop ads?
This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech - MetaMediumData ScientistHow would you measure Group Call success?
This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow would you predict a car’s turning intention?
This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).
Machine LearningOnsite - MetaMediumData ScientistIdentify 3-Person Call Cycles in Video-Calling App
This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistIdentify Algorithms for Detecting Malicious Duplicated Content
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 Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - MetaHardData ScientistIdentify Features for Fake News Detection on Facebook
Identify Features for Fake News Detection on Facebook evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaMediumData ScientistIdentify Metrics to Detect Fake-Account Activity on Facebook
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaMediumData ScientistIdentify Top 10 Users by Average Call Duration
This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.
Data Manipulation (SQL/Python)Onsite - MetaHardData ScientistIdentify non-table data for feature demand
The question evaluates a data scientist's competency in triangulating product demand from non-table sources, defining and de-biasing signals, specifying metrics and guardrails, and designing evidence-driven experiments within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistInterpreting confidence intervals to choose a treatment
Evaluates confidence-interval interpretation for choosing among feed-ranking treatments with engagement losses. Strong answers compare statistical significance, magnitude, uncertainty, business thresholds, downside risk, and launch recommendations.
Analytics & ExperimentationOnsite - MetaHardData ScientistInvestigate Reasons for Higher Instagram Story Consumption
Evaluates diagnostic analytics for explaining why Instagram users consume more Stories than Facebook users. Strong answers validate metric parity, decompose supply and demand drivers, use cohort and within-user comparisons, and propose experiments for UI, ranking, content, or notification hypotheses.
Analytics & ExperimentationOnsite - MetaEasyData ScientistInvestigate why an advertiser’s spend decreased
This question tests your ability to systematically isolate the root cause of a drop in advertising spend by decomposing metrics and evaluating external versus internal factors.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics DecompositionAdTech - MetaHardData ScientistJustify building a new feature with evidence
This question evaluates a data scientist's product analytics, experimental design, and strategic decision-making skills, covering market sizing, competitive analysis, privacy-aware data and infrastructure assessment, risk identification, and A/B testing planning.
Analytics & ExperimentationOnsite - MetaHardData ScientistLead a product deep dive with quantified impact
This question evaluates product leadership, product analytics, experimentation, and impact-measurement competencies for a Data Scientist, emphasizing problem framing, metric definition, trade-off reasoning, risk mitigation, and the ability to quantify and communicate end-to-end results.
Behavioral & LeadershipOnsite - MetaMediumData ScientistMaster Behavioral Interview Questions for Data/ML Roles
Master Behavioral Interview Questions for Data/ML Roles evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumData ScientistMeasure Harmful Content Impact with Key Metrics
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 Measure Harmful Content Impact with Key Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistMeasure a friend-recommendation launch
This question evaluates experiment design, metric selection, causal inference and business-metric translation for social features, including defining primary and guardrail metrics, choosing experimental units to mitigate network interference, and estimating revenue impact from observed lifts.
Analytics & ExperimentationOnsite - MetaMediumData ScientistMeasure and mitigate notification spam
This question tests the ability to design an experimentation framework for notification systems while accounting for user fatigue, long-term ecosystem health, and signal interference.
Analytics & ExperimentationTechnical ScreenExperimentationProduct MetricsCausal Inference - MetaHardData ScientistMeasure impact of bot mitigation via experiment
This question evaluates experiment design and causal inference skills for platform analytics, covering interference management, metric and guardrail definition, power and duration calculations, eligibility and exclusions, novelty/adaptation considerations, and diagnostics for bot‑mitigation interventions.
Analytics & ExperimentationOnsite - MetaMediumData ScientistMeasure scheduled posts feature success
This question evaluates proficiency in Bayesian statistical estimation, experimental design, product metric definition, and causal/confounder analysis for measuring feature reliability and engagement.
Analytics & ExperimentationOnsite
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