Data Scientist Interview Questions

Showing 751–780 of 1076 results

• PayPalEasy·Data Scientist
  Design and evaluate a fraud detection strategy

  This problem tests your ability to design a holistic fraud detection strategy by balancing technical model metrics with business-critical operational trade-offs.

  Analytics & ExperimentationTechnical ScreenFraud DetectionClassificationMetrics
• PayPalHard·Data Scientist
  Explain differences between Python list and tuple

  Evaluates understanding of Python data structures—specifically lists versus tuples—by probing concepts such as mutability and its implications, high-level performance and memory trade-offs, typical use cases, and hashability for use as dictionary keys.

  Coding & AlgorithmsTechnical Screen
• PayPalEasy·Data Scientist
  How to evaluate a new homepage feature

  A PayPal data science technical screen on designing an A/B test to evaluate a new homepage feature. It covers experiment design and randomization, primary/secondary/guardrail metric selection with tradeoffs, sample-size and power estimation, and pitfalls like novelty effects and interference. The solution emphasizes value-aligned metrics (payment completion over CTR) and fraud guardrails specific to a payments product.

  Analytics & ExperimentationTechnical Screen
• PayPalMedium·Data Scientist
  Implement sliding-window device anomaly

  This question evaluates understanding of streaming algorithms, sliding-window state management, per-user data structures, and handling out-of-order events under strict time and space complexity constraints.

  Coding & AlgorithmsTechnical Screen
• PayPalHard·Data Scientist
  Interpret p-values and common pitfalls

  Evaluates interpretation of p-values, hypothesis testing, experimental design and statistical pitfalls in a fraud/experimentation context—including concepts like effect size, statistical power, multiple comparisons, delayed/rare outcomes, and imperfect randomization; category/domain: Statistics & Math.

  Statistics & MathTechnical Screen
• PayPalMedium·Data Scientist
  Write SQL to flag Venmo ATO

  This question evaluates a candidate's ability to construct complex SQL to detect account-takeover (ATO) signals in a payments platform, assessing skills in event-time windowing, deduplication, joins across user/login/transaction tables, and categorical flagging of new devices or IPs.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• 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
  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
  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
• 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
• 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
• PinterestHard·Data Scientist
  Explain your ML project end-to-end

  This question evaluates a data scientist's end-to-end machine learning competencies, including problem framing and metric justification, data sourcing and labeling, model selection and calibration, class-imbalance handling, deployment and monitoring, experimentation design, and post-mortem analysis; it is in the Machine Learning domain and tests both conceptual understanding and practical application across modeling and MLOps. It is commonly asked to assess an interviewee's ability to justify trade-offs, reason about operational constraints such as latency, fairness and cost, design valid evaluation and A/B testing strategies, and define measurable monitoring and rollback criteria.

  Machine LearningTechnical Screen
• PinterestMedium·Data Scientist
  Find the Share of Power Listeners per Region from Play Events

  Write a PostgreSQL query that, for a trailing 7-day window, classifies listeners per region as active or 'power' users based on how many distinct days they listened and whether they used at least three different playback devices on any single one of those days, then reports the power-user share per region.

  Data Manipulation (SQL/Python)Technical Screensqlwindow-functionsaggregation
• PinterestMedium·Data Scientist
  Find top video category by average time

  This question evaluates a candidate's ability to perform data manipulation and aggregation in Python (pandas), covering skills such as filtering, handling nulls and nonpositive values, mapping categorical IDs to names, rounding numerical results, and applying tie-breaking rules.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestEasy·Data Scientist
  How to evaluate a new Carousel feature

  This question tests your ability to design an A/B test for a feed-based UI feature, focusing on metric selection, counter-metrics, and the identification of potential cannibalization effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsExperiment Design
• PinterestMedium·Data Scientist
  How would you evaluate a carousel launch?

  This question tests your ability to design a comprehensive evaluation framework for a product feature launch, specifically focusing on metric selection, causal impact measurement, and addressing potential cannibalization effects.

  Analytics & ExperimentationTechnical ScreenExperimentationMetricsProduct Analytics
• PinterestMedium·Data Scientist
  Implement Jackknife and Random Choice

  Implement weighted random selection from scratch and compute delete-1 jackknife estimates of parameter bias and standard error for an arbitrary statistical estimator.

  Coding & AlgorithmsTechnical Screensamplingresamplingstatistics
• PinterestHard·Data Scientist
  Implement and Evaluate Pin Similarity in Python

  Practice a DS-focused Python similarity question using Pin representations, cosine similarity, top-k ranking, and explicit edge-case handling. Extend the implementation discussion to vectorized scaling, approximate retrieval, offline ranking metrics, leakage checks, diversity, and online evaluation.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Implement and extend My Calendar III

  This question evaluates competency in designing and implementing efficient dynamic interval data structures, handling correctness for duplicate and nested intervals, extending APIs with cancellation and point queries, and performing rigorous worst-case time/space analysis and resource-adaptation.

  Coding & AlgorithmsTechnical Screen
• PinterestEasy·Data Scientist
  Implement np.random.choice

  This problem tests your ability to implement weighted random sampling from an array, focusing on probability distribution handling and efficient selection algorithms.

  Coding & AlgorithmsTechnical ScreenProbabilitySamplingAlgorithms
• PinterestMedium·Data Scientist
  Implement weighted random choice

  This problem tests your ability to implement efficient weighted random selection, specifically focusing on cumulative distribution functions and binary search to achieve optimal query performance.

  Coding & AlgorithmsTechnical ScreenAlgorithmsProbabilityBinary Search
• PinterestMedium·Data Scientist
  Interpret A/B results for video-pin increase

  This question evaluates statistical inference and experimentation skills, including A/B test hypothesis formulation with a predefined MDE, estimation of absolute and relative lift with confidence intervals, proportion testing for CTR and retention, sample ratio mismatch detection, and multiple-comparison adjustments.

  Analytics & ExperimentationTechnical Screen
• PinterestMedium·Data Scientist
  Write Queries for Pinterest Engagement Tasks

  This question evaluates SQL-based data manipulation and Python set/co-occurrence computation skills, covering aggregation, joins, timestamp-based cohorting and windowing, freshness detection, CTR calculation, and similarity scoring within a data scientist context.

  Data Manipulation (SQL/Python)Technical Screen
• PlaystationMedium·Data Scientist
  Design Experiments Under Network Interference

  This problem tests your understanding of causal inference in networked environments, focusing on how to detect and mitigate bias when treatment effects spill over between connected users in a social gaming platform.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingNetwork Science