Data Scientist Interview Questions

Showing 1021–1050 of 1971 results

• 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
• MetaMedium·Data Scientist
  Compute fraud probabilities with Bayes and Binomial

  This question evaluates a candidate's understanding of probabilistic modeling and statistical decision-making, focusing on the Binomial distribution for session-level events and Bayes' theorem for posterior probabilities in a fraud-detection setting.

  Statistics & MathOnsite
• 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
• MetaHard·Data Scientist
  Compute p-values, power, and adjust errors

  This question evaluates statistical reasoning and applied inference skills including hypothesis testing for proportions with sequential monitoring, multiple-testing control (Benjamini–Hochberg vs Bonferroni), Bayesian posterior estimation for classification flags, and robust AOV estimation and transformation techniques.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Compute posterior and event counts in fraud screen

  This question evaluates a candidate's competence in probability and statistical inference, covering binomial probability calculations, Bayes' theorem for posterior probability, rare-event base-rate reasoning, and modeling of correlated binary signals in a fraud-screening context.

  Statistics & MathOnsite
• 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 probabilities for chatbot response quality

  This question evaluates a candidate's grasp of basic probability concepts, including independence, joint event probabilities, and i.i.d. assumptions. It is commonly asked to test probabilistic reasoning about repeated trials within the Statistics & Math domain and is primarily a conceptual understanding problem with a simple practical application.

  Statistics & MathOnsite
• 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
• MetaEasy·Data Scientist
  Compute this-year spend share of last-year whales

  This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationCohort Analysis
• MetaMedium·Data Scientist
  Compute time-spent percentage by app category

  This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.

  Data Manipulation (SQL/Python)Onsite
• 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
  Construct a 95% Confidence Interval for Comment Counts

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• 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
• MetaHard·Data Scientist
  Decide when CTR falls but revenue rises

  This question evaluates understanding of A/B experimentation, auction-based ad metrics, metric decomposition for revenue impact, causal diagnostics for hidden heterogeneity (e.g., Simpson’s paradox), and executive-level results presentation within the Analytics & Experimentation domain.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Define Product Metrics: Align Stakeholders, Measure Success, Improve Results

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Define Product Metrics: Align Stakeholders, Measure Success, Improve Results states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• 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
• MetaMedium·Data Scientist
  Define and compute shop visibility in SQL

  This question evaluates the ability to define and compute shop-level visibility KPIs in SQL, testing skills in metric design (visibility_rate and impression_share), deduplication, session/window filtering, and data-quality concerns such as late-arriving events and bot filtering.

  Data Manipulation (SQL/Python)Onsite
• MetaEasy·Data Scientist
  Define engagement metrics and analyze comment distribution

  This question evaluates a data scientist's competency in metric framework design, distributional analysis of heavy-tailed user behavior, monitoring and anomaly detection, and experimental setup for measuring healthy engagement, including considerations like unit of analysis, treatment of heavy users, and anti-spam guardrails.

  Analytics & ExperimentationOnsite
• 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
• MetaMedium·Data Scientist
  Demonstrate leadership in cross-functional collaboration

  The Meta Data Scientist onsite behavioral & leadership round: answer a set of STAR-format leadership prompts spanning cross-functional collaboration and conflict, giving/receiving feedback, disagree-and-commit, being wrong and course-correcting, overcoming obstacles and winning skeptics, driving breakthroughs, ambiguous work under time pressure, breaking-metric triage, learning from peers, diversity & inclusion, inclusive meetings, proactive alignment, and building long-term trust. Each answer must name stakeholders, state trade-offs, and quantify impact against a baseline.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Deploy multi-armed bandits safely

  This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.

  Machine LearningOnsite