Data Scientist Interview Questions

Showing 1411–1440 of 1971 results

• PayPalEasy·Data Scientist
  Write SQL using HAVING and window functions

  This question tests the ability to filter aggregated data using the HAVING clause and compute relative metrics using window functions to identify high-frequency transaction patterns.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregations
• PayPalMedium·Data Scientist
  Write conditional aggregates with CASE WHEN

  Write conditional aggregates with CASE WHEN evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Write conditional aggregation SQL queries

  This question evaluates proficiency with conditional aggregation in SQL, including the use of SUM(CASE WHEN ...) versus filtering with WHERE and the implications of placing aggregation filters in HAVING for grouped queries, within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• PerplexityMedium·Data Scientist
  Implement Stratified Sampling by Country

  Implement proportional stratified sampling on an in-memory list of records partitioned by a categorical key, ensuring exact sample size via the largest remainder method and reproducible selection.

  Coding & AlgorithmsTechnical Screenalgorithmsstatisticssampling
• PinterestMedium·Data Scientist
  Analyze Global Engagement and Impressions with SQL Queries

  This question evaluates proficiency in SQL-based data manipulation and analytical querying, covering aggregation, grouping, ranking, and the correlation of impressions with user engagement and feature usage metrics.

  Data Manipulation (SQL/Python)Onsite
• PinterestHard·Data Scientist
  Analyze a geo rollout and interpret charts

  This question evaluates causal inference and product analytics competencies — specifically specification of causal estimands and difference-in-differences, segmented (interrupted) time-series regression, power and duration calculations, spillover and donor-pool diagnostics, and selection of guardrail metrics.

  Analytics & ExperimentationOnsite
• PinterestHard·Data Scientist
  Analyze survey with gender imbalance

  Analyze survey with gender imbalance 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
• PinterestMedium·Data Scientist
  Calculate 95% Bootstrap Confidence Interval for Order Values

  This question evaluates a data scientist's competence in statistical inference using bootstrap resampling, proficiency with numerical computing for large sample operations, and attention to performance optimization.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Calculate the Percentage of Highly Active Users by Country

  Calculate highly active users by country over seven calendar days using distinct active dates and same-day surface diversity, with percentage output.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Clean and Aggregate Transactions for Finance Dashboard

  This question evaluates data cleaning and transformation competencies including pandas-based vectorized operations, categorical mapping and feature flagging, as well as aggregation and summarization of transactional data in the data manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Compute CTR by format for new US users

  This question evaluates a candidate's competency in SQL data manipulation and aggregation, covering joins, date-based filtering for user cohorts, deduplication, conditional summing, null-safe arithmetic, rounding, and ordering to compute a business metric (CTR).

  Data Manipulation (SQL/Python)Technical Screen
• PinterestEasy·Data Scientist
  Compute average unique pins per user

  This question evaluates a candidate's ability to process collections and compute aggregated metrics, including per-user deduplication of items, handling of missing/null entries, numeric averaging and rounding.

  Coding & AlgorithmsTechnical Screen
• PinterestHard·Data Scientist
  Connect Probability, Experiment Sizing, and Bootstrap Inference

  Tests end-to-end statistical rigor across discrete probability modeling, sample size derivation for A/B testing, bootstrap confidence intervals for heavy-tailed engagement metrics, and translating conflicting inferential signals into product launch recommendations.

  Statistics & MathOnsiteStatisticsExperimentationA/B Testing
• PinterestHard·Data Scientist
  Decide if ad load is optimized

  This question evaluates competency in analytics and experimentation, covering experiment design, metric and guardrail definition, causal inference, statistical testing, segmentation for heterogeneous effects, and balancing user experience versus monetization.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Decode and Explain Ambiguity in Compression Strings

  This question evaluates parsing of compact count-value encodings, reasoning about ambiguous encodings, and combinatorial enumeration of all valid decodings.

  Coding & AlgorithmsOnsite
• PinterestHard·Data Scientist
  Demonstrate leadership with concrete STAR examples

  This question evaluates leadership, stakeholder influence, metric-driven decision-making, ambiguity management, end-to-end analytical ownership, and resilience in a Data Scientist context, categorized under Behavioral & Leadership.

  Behavioral & LeadershipOnsite
• PinterestMedium·Data Scientist
  Design Algorithm to Minimize Payments in Expense-Sharing App

  This question evaluates algorithm design and optimization skills, focusing on modeling multi-party transactions and minimizing the number of settlement operations.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Design Data Structure for Sparse Matrices Operations

  This question evaluates a candidate's ability to design efficient data structures and implement algorithms for sparse matrix storage and operations, emphasizing space-efficient representations, matrix arithmetic, and computational complexity reasoning.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Design and Evaluate a Home Carousel

  This question evaluates a candidate's expertise in product analytics, experimentation design, metric definition, and instrumentation for measuring the impact of a new UI feature.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Design and Interpret a Video Pin Experiment

  This question tests the ability to design a balanced A/B experiment for a two-sided marketplace (creators and viewers) and synthesize conflicting metrics into a launch decision.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct MetricsGuardrails
• PinterestMedium·Data Scientist
  Design and assess a video-pin increase experiment

  A Pinterest Data Scientist technical-screen question on Analytics & Experimentation: design a rigorous A/B test for increasing video-pin share in the Home Feed — unit of randomization, primary metric and guardrails, power/MDE, sequential analysis, and a quasi-experiment fallback — then interpret two 14-day readouts and recommend ship, iterate, or stop. It rewards balancing engagement lifts (CTR) against guardrail regressions (retention, complaints, crash rate), multiple-testing robustness, and cross-unit (per-impression vs per-session) trade-offs.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Design metrics and experiment for Shopping launch

  This question evaluates a data scientist’s competency in experimental design and product analytics, including metric selection and precise definitions, guardrail and funnel diagnostics, spillover and novelty handling, power/MDE estimation, and multi-objective decision framing.

  Analytics & ExperimentationTechnical Screen
• PinterestHard·Data Scientist
  Design rigorous A/B test and causal analysis

  This question evaluates a data scientist's competency in experimental design, sample-size and power calculations, variance-reduction methods (e.g., CUPED), sequential testing and alpha spending, clustering and interference effects, SRM checks, and causal identification strategies such as DID, IV, and RDD within the Statistics & Math domain.

  Statistics & MathOnsite
• PinterestMedium·Data Scientist
  Determine Pin Connections Through Common Boards

  This question evaluates graph modeling and connectivity concepts, including representing boards and pins as relationships and reasoning about reachability and minimal connection paths.

  Coding & AlgorithmsOnsite
• PinterestHard·Data Scientist
  Diagnose CTR drop after recommendation launch

  This question evaluates a data scientist's competency in experiment diagnosis, metric and guardrail definition, instrumentation validation, segmentation analysis, and causal reasoning for recommender-system impacts.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Estimate a Launch Impact with Difference in Differences

  Tests your ability to design and evaluate a regional quasi-experiment using Difference-in-Differences (DiD), validate key identifying assumptions, structure robust inference, and address modern complications such as staggered rollout and network spillovers.

  Analytics & ExperimentationOnsiteDifference-in-DifferencesCausal InferenceQuasi-Experiments
• PinterestHard·Data Scientist
  Estimate billboard reach and impressions

  This question evaluates probabilistic modeling and statistical inference skills applied to audience measurement and attribution, covering Poisson-based frequency modeling, visibility-adjusted impressions, panel-based deduplication and expansion, uncertainty propagation (delta method/bootstrap), and Markov-chain attribution within the Statistics & Math / Data Science domain. It is commonly asked to test the ability to convert traffic and visibility inputs into quantitative reach and impressions with propagated confidence intervals, reason about attribution via a 3-state Markov chain, identify measurement biases, and demonstrates both conceptual understanding and practical application through numerical estimation and sensitivity analysis.

  Statistics & MathOnsite
• PinterestMedium·Data Scientist
  Evaluate Carousel and Billboard Lift

  This question tests your ability to design an A/B test for multi-format advertising, specifically handling cannibalization and cross-format lift measurement.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMetrics
• PinterestMedium·Data Scientist
  Evaluate Fresh Content and Video Experiments

  This question evaluates competencies in metric design, experimental design, statistical inference, and product analytics—specifically defining and critiquing freshness metrics, formulating null and alternative hypotheses for content changes, interpreting A/B test results, and calculating sample size and power.

  Analytics & ExperimentationTechnical Screen
• PinterestMedium·Data Scientist
  Evaluate New Feed-Ranking Algorithm with A/B Testing

  Pinterest experimentation prompt on evaluating a new feed-ranking algorithm, covering A/B hypotheses, daily active minutes, guardrails, sample size, MDE, experiment health, mid-test dips, and geo-based causal inference.

  Analytics & ExperimentationOnsite