Data Scientist Interview Questions

Showing 991–1020 of 1971 results

• MetaMedium·Data Scientist
  Calculate French User Engagement and U.S. Call Duration

  This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaEasy·Data Scientist
  Calculate Posterior Fraud Probability Using Bayes' Theorem

  Calculate Posterior Fraud Probability Using Bayes' Theorem 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
• MetaEasy·Data Scientist
  Calculate Posterior Probability of Flagged User Being Bad Actor

  Evaluates Bayesian inference and confusion-matrix reasoning for abuse detection. Strong answers derive posterior precision from prevalence, TPR, and FPR, define Type I and Type II errors, and compute true positives and false positives for 10 million users.

  Statistics & MathOnsite
• MetaEasy·Data Scientist
  Calculate Probability of Honest and Relevant Chatbot Answers

  Calculate Probability of Honest and Relevant Chatbot Answers 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
• MetaMedium·Data Scientist
  Calculate Survey Response Rate and Quality Metric in SQL

  This question evaluates SQL data-manipulation competencies such as aggregation, NULL handling, deduplication (first-per-group), and computing engagement and quality metrics from event logs.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Total Revenue in USD Using SQL Query

  This question evaluates proficiency in SQL data manipulation, focusing on joins, aggregations, and handling currency conversion across relational tables.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Calculate Video Call Usage Metrics by Country and Date

  This question evaluates data manipulation and product-analytics skills, focusing on joining user and call datasets, aggregations for distinct-user percentages, and per-DAU duration calculations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate and Compare Survey Response Rates for User Tenure

  This question evaluates proficiency in relational data manipulation and metric computation, including SQL joins, aggregation and cohort-based comparisons relevant to survey response analysis, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Characterize and compare transfer-count distributions over time

  This question evaluates a candidate's ability to model zero-inflated, heavy-tailed count data, interpret distributional summaries and percentiles, reason about temporal shifts from retention and fraud dynamics, and identify robust summary statistics and diagnostics for a Data Scientist role.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Characterize metric distribution and quantiles

  This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.

  Statistics & MathOnsite
• MetaHard·Data Scientist
  Choose alternatives when randomization fails

  This question evaluates proficiency in causal inference and experimentation analytics for a Data Scientist role, focusing on selecting appropriate identification strategies, designing placebo and pre‑trend tests, and specifying robustness and sensitivity analyses for non‑randomized feature adoption.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Choose and compute recommender evaluation metrics

  This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.

  Machine LearningHR Screen
• MetaHard·Data Scientist
  Choose group-call size cap via experiment

  This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Choose threshold under asymmetric costs

  This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Clarify scope and align to mission

  This question evaluates a data scientist's product sense, leadership and stakeholder-alignment skills, including scope clarification, mission translation, metric definition, and collaborative communication.

  Behavioral & LeadershipTechnical Screen
• MetaHard·Data Scientist
  Communicate trade-offs and influence launch

  This question evaluates product judgment, experimentation interpretation, and cross-functional leadership skills for a data scientist, focusing on communicating trade-offs between per-account metric lifts and cross-account cannibalization; Category: Behavioral & Leadership, Domain: data science and product experimentation.

  Behavioral & LeadershipTechnical Screen
• MetaEasy·Data Scientist
  Compare Ad-Insertion Strategies: Expected Ads and Probabilities

  Evaluates probability, expectation, and variance for newsfeed ad-insertion strategies. Strong answers compute expected ad load, at-least-one exposure probability, and explain why equal expected counts can still produce different user experiences.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compare Bayesian and frequentist decisions

  This question evaluates understanding of Bayesian inference versus frequentist hypothesis testing, decision theory under asymmetric loss, sequential monitoring/optional stopping, and prior elicitation applied to A/B testing within the Statistics & Math domain.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compare Instagram and Facebook Stories Using Key Performance 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 Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Compare Instagram vs. Facebook using causal experiments

  This question evaluates causal inference, experimentation design, metric engineering, and ecosystem-level impact assessment skills for a Data Scientist in the Analytics & Experimentation domain, with emphasis on defining a single-objective OEC that prevents pure cannibalization and on measuring cross-app effects via randomized rollouts.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Compare Survey Satisfaction for New and Established Users

  Tests your ability to write PostgreSQL queries that segment users into cohorts based on date logic, filter invalid survey events, and calculate aggregate satisfaction metrics.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqldata-aggregation
• MetaMedium·Data Scientist
  Compute 7-day views and reactions by relationship

  This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MetaEasy·Data Scientist
  Compute Bayes probability for fake accounts

  This question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.

  Statistics & MathOnsiteBayes TheoremProbabilityStatistics
• MetaMedium·Data Scientist
  Compute CTR overall and by campaign type

  This question evaluates data manipulation and analytics skills, specifically SQL aggregations, deduplication of event logs, time-window filtering, joins to campaign metadata, and safe ratio computation including division-by-zero handling.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute Each Advertiser's Share of Shop Ad Spend

  This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationWindow Functions
• MetaHard·Data Scientist
  Compute High-Call Usage Rates

  This question evaluates a candidate's ability to perform advanced data manipulation and analytical SQL tasks, including joins, aggregations, timezone-aware date handling, rolling window metrics, distinct user counting, and counting user participations across roles.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute SHOP spend share and model performance

  This question evaluates proficiency in SQL/Python data manipulation and analytics, focusing on aggregations, joins, time-window computations, handling missing or zero values, and deriving key metrics such as revenue share and revenue-per-conversion.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute Shop Visibility Rate Using SQL and Python

  This question evaluates proficiency in data manipulation and metric design using SQL and Python, focusing on event-level aggregation, user deduplication, and definition of a business visibility metric within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• MetaEasy·Data Scientist
  Compute active ad revenue by creation source

  This question evaluates proficiency in data manipulation and analytics, testing skills such as joining event and impression tables, aggregating time-series revenue, and counting distinct active ads broken down by creation source.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Compute ad impression conversion rates

  This question evaluates proficiency in time-based event attribution, aggregation of business metrics (impressions, conversion rates, spend and revenue), timestamp handling, and user/ad-level matching within the Data Manipulation (SQL/Python) domain, emphasizing practical application over abstract theory.

  Data Manipulation (SQL/Python)Onsite