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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 391–420 of 641 results
- MetaEasyData ScientistExplain why IG Story usage exceeds Facebook
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 Explain why IG Story usage exceeds Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistFake Accounts [AE]
This question tests your ability to apply Bayes' Theorem in a low-prevalence classification scenario and your ability to engineer robust signals for detecting anomalous user behavior.
Statistics & MathOnsiteBayes TheoremClassificationPrecision-Recall - MetaMediumData ScientistFind least active countries
This question tests the ability to perform multi-step data aggregation using SQL, specifically focusing on joining, filtering by date ranges, calculating distinct counts, and identifying bottom-tier performers across different time periods.
Data Manipulation (SQL/Python)OnsiteSQLAggregationJoins - MetaMediumData ScientistFind the most-used app
This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.
Data Manipulation (SQL/Python)Onsite - 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 - 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 - 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 - 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 - 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 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 - 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 - MetaMediumData ScientistModel session times and comments with exponential/Poisson
This question evaluates understanding of memoryless processes and count-data modeling, covering exponential (constant-hazard) distributions for session durations and Poisson approximations for comment counts within survival analysis and count-data modeling in Statistics & Math for a Data Scientist role.
Statistics & MathOnsite - MetaMediumData ScientistOptimize Oculus Data Streaming with Bandwidth Constraints
This question evaluates a candidate's ability in algorithm design and problem decomposition for array segmentation and interval coverage, focusing on reasoning about cumulative constraints for streaming data and geometric interval intersections.
Coding & AlgorithmsOnsite - MetaMediumData ScientistPromote Inclusion and Overcome Barriers in Social-Commerce Team
Promote Inclusion and Overcome Barriers in Social-Commerce Team 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 - MetaHardData ScientistProve source growth is cannibalization, not incremental
This question evaluates causal inference and experimental-design competencies in analytics, testing skills in identification strategy, treatment and control definition, specification of difference‑in‑differences or randomized geo experiments, metric construction, power calculations, and robustness checks for attribution and cannibalization analysis. It is commonly asked to determine whether observed growth is incremental or substitution-driven, falls under the Analytics & Experimentation domain in Data Science, and emphasizes practical application accompanied by conceptual understanding of identification assumptions, statistical power, and measurement.
Analytics & ExperimentationOnsite - MetaMediumData ScientistRank Ads by Conversion Rate for Top 10 Performers
This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistRedesign an executive dashboard for C-suite
This question evaluates skills in dashboard design, data visualization, metric selection, and executive-level analytical communication for product and growth measurement.
Analytics & ExperimentationOnsite - MetaMediumData ScientistReduce variance with covariate adjustment
This question evaluates a data scientist's competency in experimental design, variance reduction techniques, and regression-based covariate adjustment (CUPED/ANCOVA) within randomized A/B tests, including understanding of statistical power and diagnostic checks.
Analytics & ExperimentationOnsite - MetaMediumData ScientistReflect on Conflict Resolution and Key Learnings
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Reflect on Conflict Resolution and Key Learnings states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaHardData ScientistReplace legacy ads model safely
This question evaluates competence in machine learning systems engineering, including offline validation, counterfactual/off‑policy evaluation, online experimentation design, and production monitoring for ad-ranking models.
Machine LearningOnsite - MetaMediumData ScientistResolve Conflicts and Convince Skeptical Stakeholders Effectively
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Conflicts and Convince Skeptical Stakeholders Effectively states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumData ScientistResolve Ties for Top-10 Users in SQL Query
This question evaluates proficiency in SQL ranking and tie-resolution techniques for generating deterministic top-N leaderboards, focusing on handling identical scores while producing an exact set of rows.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistResolve cross-team conflict and align incentives
This question evaluates a Data Scientist's ability to resolve cross-team conflicts through stakeholder mapping, incentive alignment, quantified negotiation planning, meeting facilitation, escalation management, and success measurement.
Behavioral & LeadershipOnsite - MetaHardData ScientistResolve exclusion, learn fast, and manage conflict
This question evaluates interpersonal leadership, cross-team collaboration, conflict resolution, rapid domain learning, stakeholder mapping, and the ability to quantify analytic trade-offs and impact within a data science context.
Behavioral & LeadershipOnsite
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