Data Scientist Interview Questions

Showing 571–600 of 1076 results

• 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 ad revenue metrics by geography in SQL

  In the Data Manipulation (SQL/Python) domain, this intermediate-level question evaluates SQL aggregation, join logic between impressions and clicks, date-based UTC attribution, user-geo joins, and null/divide-by-zero-safe computation of derived metrics (CTR, RPM) at the country-day granularity.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute and correct correlation significance inflation

  This question tests the understanding of how multiple hypothesis testing increases the risk of false positives (Type I error) in high-dimensional correlation matrices and requires the application of correction methods to maintain statistical integrity.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingCorrelation
• MetaMedium·Data Scientist
  Compute binary-tree diameter via return-only DFS

  This question evaluates algorithmic problem-solving with binary trees, focusing on tree traversal techniques, recursion versus iterative implementations, and analysis of time and space complexity including worst-case input shapes such as skewed trees.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Scientist
  Compute cohort GMV and payer rate with edge cases

  This question evaluates a data scientist's competency in cohort-based GMV calculation, payer-rate measurement, and handling common edge cases across events, orders, payments, and refunds.

  Data Manipulation (SQL/Python)Technical Screen
• MetaEasy·Data Scientist
  Compute multi-account user distribution and unread pct

  This question evaluates a data scientist's ability to perform relational data manipulation and aggregation with SQL or Python, covering competencies such as handling one-to-many joins, grouping and bucketing counts, deduplicating across related tables, and computing cohort-level percentages from boolean flags.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute posterior fake probability using Bayes' rule

  The problem evaluates understanding of Bayes' rule and conditional probability within the Statistics & Math domain for a Data Scientist role, at an introductory applied probability/interpretation level.

  Statistics & MathTechnical Screen
• MetaEasy·Data Scientist
  Compute probability an account is fake

  This question tests the ability to apply Bayes' Theorem to update the probability of a hypothesis (an account being fake) given new evidence (an automated flag).

  Statistics & MathTechnical ScreenBayesian StatisticsConditional ProbabilityClassification Metrics
• MetaHard·Data Scientist
  Compute sample size and test duration correctly

  This question evaluates competency in experiment design and statistical power calculations, including sample size estimation, variance misspecification effects, multiple-comparison corrections, sequential monitoring boundaries, and adjustments for covariates or clustering.

  Statistics & MathTechnical Screen
• MetaMedium·Data Scientist
  Compute shop visibility and intent metrics in SQL

  This question evaluates SQL-based data manipulation and analytical competencies in the Data Manipulation (SQL/Python) domain, focusing on deduplication, time-zone-aware timestamp handling, window functions, aggregation, and construction of visibility, CTR, and intent metrics for shops.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute unconnected 60s posts and reactions averages

  This question evaluates data manipulation skills in SQL and Python, focusing on deduplication, join logic between event and reaction tables, date handling for string-typed dates, and aggregation to compute per-post metrics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute unread and multi-account user percentages

  This question evaluates understanding of SQL data manipulation skills such as aggregations, joins, NULL handling, deduplication reasoning, and user- versus person-level computations for accurate percentage metrics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute view prevalence from views and labels

  This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Count heavy callers in 7 days

  This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow-FunctionsData-Manipulation
• MetaMedium·Data Scientist
  Decide and experiment on Group Call feature

  This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Decide event notification launch via experiments

  This question tests the ability to design a robust A/B test in the presence of network effects, interference, and cannibalization within a social notification ecosystem.

  Analytics & ExperimentationTechnical ScreenA/B TestingNetwork EffectsMetrics
• MetaMedium·Data Scientist
  Define Success for a New Group Feature Without Hiding Cannibalization

  Design a comprehensive measurement and experimentation framework for a new community feature, evaluating true incremental value while detecting cannibalization across existing surfaces and mitigating network spillover effects.

  Analytics & ExperimentationTechnical Screenexperimentationmetric-designcannibalization
• MetaHard·Data Scientist
  Deliver an elevator pitch and impact example

  This question evaluates a data scientist's communication and leadership competencies, including concise elevator pitching, product sense, end-to-end experimentation design and statistical rigor, causal reasoning, and the ability to quantify measurable business impact within a product context.

  Behavioral & LeadershipTechnical Screen
• MetaHard·Data Scientist
  Design Messenger spam experiment with clustering

  This question evaluates a data scientist's skills in experimental design, causal inference, clustering and interference analysis, metric specification, and power/sample-size calculation within the Analytics & Experimentation domain for Data Scientist roles.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Design Metrics to Track and Analyze Spam 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 Design Metrics to Track and Analyze Spam Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Design SQL Query for Shop Visibility and User Activity Metrics

  This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Design a clustered notification experiment with guardrails

  This question evaluates experimental-design and causal-inference competencies for cluster-randomized experiments, covering metric selection, power and design-effect calculations, intracluster correlation, spillover detection, sequential monitoring, guardrails, and operational rollout within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to determine how applicants balance statistical rigor and real-world operational constraints—such as cluster choice, monitoring thresholds, and analysis strategy—testing both conceptual understanding and practical application in production experimentation.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design an A/B Test for Group Video Calls 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 Design an A/B Test for Group Video Calls Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design an A/B test for pinned-unread feature

  This question evaluates proficiency in experiment design and causal inference for product analytics, including choosing the randomization unit, defining eligibility and exposure, selecting primary and guardrail metrics, performing power/MDE and sample-size analysis, and addressing identity cross-contamination and network effects.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design an experiment to evaluate a new ads algorithm

  This question evaluates a data scientist's competency in experimental design, causal inference, metric selection and trade-offs, randomization and interference reasoning, rollout strategies, and observational methods for algorithm evaluation, including consideration of stakeholder impacts such as users, advertisers, and revenue.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design analytics and experiment for group video calls

  This question evaluates product analytics and experimentation competencies, including tie-strength and group-affinity measurement, event-level instrumentation design, proxy-product forecasting, participant-cap constraints and operational trade-offs, and experiment design that accounts for network effects and contamination.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Design and analyze a group-calls experiment

  This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design cluster-randomized test under network effects

  This question evaluates a data scientist's experiment design and causal inference skills under interference, covering exposure and estimand definition, graph-based cluster construction, cluster-level randomization and contamination mitigation, metric selection, and design-effect/sample-size calculations.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Design experiment for Group Calls with interference

  This question evaluates a data scientist's competency in experimental design and causal inference for networked features, covering metrics selection (primary and guardrail metrics), strategies for mitigating interference via randomization or quasi-experimental approaches, exposure and contamination controls, rollout planning, and pre-registration and power analysis. Commonly asked in Analytics & Experimentation interviews for Data Scientist roles, it assesses reasoning about bias–variance trade-offs, engineering constraints, and validity diagnostics in interconnected user environments, requiring both conceptual understanding of identification and practical application of experiment implementation.

  Analytics & ExperimentationTechnical Screen