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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 961–990 of 1971 results
- MetaHardData ScientistAnalyze Change in App Metrics and Feature 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 Analyze Change in App Metrics and Feature Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Comment Distribution Using Statistical Metrics and Tests
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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Hashtag Follow Behavior with SQL Queries
This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Key Metrics for Notification System Success
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 Analyze Key Metrics for Notification System Success states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Mobile Promo Orders with SQL Query and Metrics
This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze Oculus App Engagement with SQL Queries
This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Revenue Shifts to Identify Cannibalization Effects
Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Seller Activity and Vehicle Listing Interactions
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze Social Media Engagement with SQL Queries
This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze Thirty-Day Ad Performance with SQL
This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.
Data Manipulation (SQL/Python)OnsiteSQLAggregationTime-Series - MetaMediumData ScientistAnalyze Top Call Initiators and Active French Video Callers
This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze User Comment Distribution and Sampling Effects
Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze User Engagement and Spammer Read-Rate in SQL
This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze User Transfer Distribution in Initial Launch Period
Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze ad targeting expectations and distributions
This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.
Statistics & MathHR Screen - MetaMediumData ScientistAnalyze skewed comments and sampling effects
This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.
Statistics & MathOnsite - MetaMediumData ScientistAnalyzing abuse in the content‑reporting system
Evaluates reporting-system analytics for valid reports and misuse detection in moderation workflows. Strong answers define distinct reported-user share, write SQL for valid reports, and detect abusive reporters through frequency, hit-rate, burst, and coordination metrics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAnnotating and forecasting a long‑tail distribution
Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.
Statistics & MathOnsite - MetaMediumData ScientistApply reinforcement learning to product decisions
This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.
Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation - MetaHardData ScientistApply sequential testing without p-hacking
This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.
Statistics & MathOnsite - MetaMediumData ScientistAssess Cultural Fit and Problem-Solving in Reality Labs Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit and Problem-Solving in Reality Labs Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumData ScientistAssess ranking change and design experiment
This question evaluates a candidate's competency in causal inference, experimentation design, metric definition, and observational analytics for product ranking and engagement problems within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAssessing whether a new metric A is meaningful for News Feed
Evaluates whether a proposed News Feed proxy metric is meaningful for long-term user value. Strong answers test predictive power, actionability, gaming risk, true-outcome alignment, collection cost, privacy, latency, and decision criteria.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistBrainstorm how to optimize email engagement
This question evaluates a data scientist's competency in lifecycle email optimization, attribution of incremental on-site engagement, experiment design, metric selection, and trade-offs involving deliverability and guardrails within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistBuild dashboard; diagnose engagement–purchase gap
This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCalculate Ad Insertion Statistics for Two Methods
Meta statistics prompt comparing two ad-insertion strategies, covering binomial moments, deterministic quotas, tail probability for excessive ads, adjacent ad pairs, and risk of long ad runs.
Statistics & MathOnsite - MetaMediumData ScientistCalculate Ad Performance with Click-Through and Conversion Rates
This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCalculate CTR and ad revenue
This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.
Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationMetrics - MetaMediumData ScientistCalculate Daily Survey Response Rates by Country
Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.
Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLData Aggregation - MetaEasyData ScientistCalculate Expected Impressions and Probability for Users
Meta probability question on random ad-impression allocation, including binomial expectation, probability a user sees at least one impression, expected unique reach, and large-population approximations.
Statistics & MathOnsite
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