Data Scientist Interview Questions

Showing 511–540 of 641 results

• 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 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
• 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
  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
• 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 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
  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 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
• Point72Medium·Data Scientist
  Explain How L1 and L2 Regularization Change Linear-Model Coefficients

  This question tests the conceptual understanding of how L1 and L2 penalties influence the optimization objective and the resulting structure of the weight vector in a linear regression model.

  Machine LearningOnsiteRegularizationLinear RegressionOptimization
• RedditEasy·Data Scientist
  Collaborate with PM and Eng as DS

  This question evaluates a data scientist's cross-functional collaboration, prioritization, ownership, stakeholder management, and influencing skills when engaging with product managers and engineers.

  Behavioral & LeadershipOnsite
• RedditEasy·Data Scientist
  Design A/B test for AI chat box

  This question tests the ability to define metrics, design an A/B testing framework, and account for potential biases when deploying a new AI-powered chat feature.

  Analytics & ExperimentationOnsiteA/B TestingProduct AnalyticsMetrics
• RedditEasy·Data Scientist
  Design experiment for ads in chat with budgets

  This question tests the ability to design an A/B test for a new ad placement in a chat feature while accounting for budget constraints and potential network effects.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceExperiment Design
• RedditHard·Data Scientist
  Estimate Incremental Search-Ad Revenue Without an A/B Test

  Design an evaluation framework to estimate the incremental search-ad revenue of a small-scale manual pilot, incorporating variance reduction, inference under heavy-tailed revenue, and ecosystem cannibalization.

  Analytics & ExperimentationOnsiteexperimentationcausal-inferencead-tech
• RedditEasy·Data Scientist
  Measure impact of ads-manager automation feature

  This question tests your ability to design an evaluation framework for a new feature, specifically focusing on causal inference, metric selection, and identifying potential biases in a two-sided marketplace.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceMetrics
• RedditEasy·Data Scientist
  Prioritize competing engineering requests

  This question evaluates a data scientist's prioritization, stakeholder management, trade-off communication, and leadership competencies when balancing competing analytics and experimentation requests.

  Behavioral & LeadershipOnsite
• RobinhoodMedium·Data Scientist
  Create OHLC Aggregates from Tick Data in Python

  This question evaluates a candidate's ability to perform time-series bucketing and OHLC aggregation from tick data, testing skills in data parsing, stateful aggregation, and handling missing-interval forward-filling; it falls under Data Manipulation (SQL/Python) and assesses practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Onsite