Data Scientist Interview Questions

Showing 1441–1470 of 1971 results

• 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 available reservation time slots

  This question evaluates a candidate's ability to reason about time-interval data and temporal normalization, testing competencies in interval manipulation, conflict detection, and algorithmic problem-solving for scheduling contexts.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Find list pair with maximum overlap

  This question evaluates proficiency with set operations and similarity metrics (Jaccard), scalable algorithm design and complexity analysis for large datasets, and the ability to handle streaming updates and deterministic tie-breaking.

  Coding & AlgorithmsOnsite
• 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
• PinterestMedium·Data Scientist
  How to Design a Proportional Randomized Sampler?

  This question evaluates proficiency in randomized algorithms and probabilistic sampling, with emphasis on numerical precision, large-weight handling, and memory-versus-accuracy trade-offs. Commonly asked in Coding & Algorithms interviews for data-science roles, it assesses practical application ability grounded in conceptual understanding of numerical stability and relevant data-structure choices.

  Coding & AlgorithmsOnsite
• 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 Binary Search for Policy Violation Logs

  This question evaluates understanding of algorithmic search and data manipulation skills, specifically applying binary search over date-ordered logs and composing efficient queries using SQL and Python.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Implement Bootstrap and Jackknife Estimates on a DataFrame

  Tests practical statistical computing skills by requiring the implementation of bootstrap confidence intervals and jackknife variance estimation for arbitrary metrics evaluated on pandas DataFrames.

  Statistics & MathOnsitestatisticsresamplingbootstrap
• PinterestMedium·Data Scientist
  Implement Data Structure for Top-K Elements in Streams

  This question evaluates competency in streaming algorithms, dynamic data structures for maintaining top-K elements in real time, and scalability considerations for handling large or distributed input streams.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Implement Function to Return First n Prime Numbers

  This question evaluates algorithmic problem-solving and basic number-theory competency by requiring generation of prime numbers with attention to correctness and performance characteristics.

  Coding & AlgorithmsOnsite
• 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
• PinterestMedium·Data Scientist
  Implement LRUCache with O(1) Operations and Thread Safety

  This question evaluates a candidate's ability to implement a constant-time LRU cache and reason about thread-safety and concurrent access, testing data structure design and concurrency competencies in the Coding & Algorithms domain.

  Coding & AlgorithmsOnsite
• 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
• PinterestHard·Data Scientist
  Investigate Homepage Experiment Without Control Group: Methods and Metrics

  Evaluates quasi-experimental analysis and product-metric judgment after a homepage launch lacks a randomized control. Strong answers compare matching and propensity weighting, audit experiment hygiene, choose metrics, and diagnose mixed CTR results.

  Analytics & ExperimentationOnsite
• PinterestMedium·Data Scientist
  Maximize Non-Overlapping Task Scheduling Efficiency

  This question evaluates understanding of interval scheduling and resource optimization, focusing on reasoning about time intervals and conflict-free selection of tasks.

  Coding & AlgorithmsOnsite
• PinterestMedium·Data Scientist
  Measure Billboard Campaign Impact: Design, Bias, Test Strategy

  This question assesses your ability to design a quasi-experimental framework for measuring the offline impact of an online brand campaign on user acquisition, accounting for external noise and selection bias.

  Analytics & ExperimentationOnsiteCausal InferenceGeo-ExperimentsMetric Design
• PinterestMedium·Data Scientist
  Optimize Hyper-parameter Search to Prevent Combinatorial Explosion

  Evaluates Python grid-search generation and hyperparameter optimization strategy. Strong answers yield lazy Cartesian-product combinations and discuss random search, Bayesian optimization, pruning, early stopping, and budget limits.

  Machine LearningOnsite
• PinterestMedium·Data Scientist
  Size an Opportunity and Present It with Clear Visuals

  Tests your ability to size an ambiguous product opportunity using top-down and bottom-up estimation, stress-test core assumptions with sensitivity analysis, and design executive-ready visuals to guide strategic investment decisions.

  Analytics & ExperimentationOnsiteopportunity sizingproduct analyticsdata visualization
• PinterestMedium·Data Scientist
  Transform nested dicts with pandas apply/lambda

  This question evaluates proficiency in pandas-based data manipulation, including flattening nested list and dict structures, parsing nested attributes, computing per-user aggregates and segment metrics, and implementing robust, vectorized extraction logic that handles missing keys and avoids SettingWithCopy pitfalls.

  Data Manipulation (SQL/Python)Onsite
• PinterestHard·Data Scientist
  Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• 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
• PinterestMedium·Data Scientist
  Write SQL to compute max-overlap lists

  This question evaluates proficiency in SQL data manipulation and set-based operations, focusing on deduplication, pairwise aggregation, and similarity metric computation (Jaccard) over relational tables.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Write SQL to rank categories by impressions

  This question evaluates proficiency in SQL data manipulation—covering joins, aggregations, grouping, tie-breaking, date-window logic, and scalability considerations such as indexing and query performance—in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Write windowed retention and ARPU SQL

  This question evaluates proficiency with SQL window functions, joins, aggregations and time-windowed analytics for computing retention and ARPU, as well as handling date truncation, UTC day boundaries and common data edge cases across user, session, and order tables.

  Data Manipulation (SQL/Python)Onsite