Pinterest interview questions

Showing 61–90 of 161 results

• Design and assess a video-pin increase experimentMedium

  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.

  PinterestData ScientistAnalytics & ExperimentationTechnical Screen
• Design highly available blob storage serviceMedium

  This question evaluates a candidate's competency in designing scalable, durable, and highly available blob/object storage systems, encompassing distributed systems architecture, data modeling for buckets and objects, metadata management, partitioning and routing, fault tolerance strategies, consistency models, large-object transfer patterns, security controls, and operational concerns like monitoring and capacity planning. Commonly asked in the System Design domain to assess practical application of architectural patterns and trade-offs—horizontal scalability, durability versus latency, consistency guarantees, and operational resilience—it primarily tests practical system-design skills grounded in conceptual understanding of distributed systems.

  PinterestSoftware EngineerSystem DesignOnsite
• Design metrics and experiment for Shopping launchHard

  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.

  PinterestData ScientistAnalytics & ExperimentationTechnical Screen
• Design notification and feed recommendersMedium

  This question tests the ability to design a dual-purpose recommendation architecture capable of handling real-time push notifications and personalized feed updates at scale.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design rigorous A/B test and causal analysisHard

  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.

  PinterestData ScientistStatistics & MathOnsite
• Determine Pin Connections Through Common BoardsMedium

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

  PinterestData ScientistCoding & AlgorithmsOnsite
• Determine equality of arbitrarily nested setsMedium

  This question evaluates understanding of nested data structures and set semantics, including canonicalization, hashing, recursion, handling of edge cases, unit-test design, and time/space complexity analysis.

  PinterestSoftware EngineerCoding & AlgorithmsTechnical Screen
• Diagnose CTR drop after recommendation launchHard

  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.

  PinterestData ScientistAnalytics & ExperimentationOnsite
• Estimate a Launch Impact with Difference in DifferencesMedium

  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.

  PinterestData ScientistAnalytics & ExperimentationOnsite
• Estimate billboard reach and impressionsHard

  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.

  PinterestData ScientistStatistics & MathOnsite
• Evaluate Carousel and Billboard LiftMedium

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

  PinterestData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Fresh Content and Video ExperimentsMedium

  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.

  PinterestData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate New Feed-Ranking Algorithm with A/B TestingMedium

  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.

  PinterestData ScientistAnalytics & ExperimentationOnsite
• Explain bias–variance, overfitting, and vanishing gradientsMedium

  This question tests the conceptual understanding of the bias-variance tradeoff, methods for diagnosing and preventing overfitting, and the theoretical causes and architectural solutions for vanishing gradients in deep learning.

  PinterestMachine Learning EngineerMachine LearningTechnical Screen
• Explain learning-rate fluctuation and vanishing gradientsEasy

  This question evaluates a candidate's understanding of optimization dynamics (effects of learning rate on training stability) and backpropagation-related issues (vanishing gradients) in deep neural networks, assessing competencies in training behavior and gradient propagation within the Machine Learning domain.

  PinterestMachine Learning EngineerMachine LearningTechnical Screen
• Explain overfitting and how to prevent itHard

  This question evaluates understanding of overfitting, the relationship between training and validation performance and generalization, and knowledge of regularization concepts including differences between L1 and L2 penalties.

  PinterestMachine Learning EngineerMachine LearningTechnical Screen
• Explain overfitting, underfitting, and regularizationHard

  This question tests the conceptual understanding of the bias-variance tradeoff and the practical application of regularization to balance model complexity and generalization error.

  PinterestMachine Learning EngineerMachine LearningTechnical Screen
• Explain your ML project end-to-endHard

  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.

  PinterestData ScientistMachine LearningTechnical Screen
• Fetch a Field from Two URLs, Extract Valid IP Addresses, and Rank Them by CountEasy

  A practical scripting exercise for a site reliability role: send GET requests with a required header to two URLs, pull a text field from each response, extract every valid IP address, and rank the addresses by how often they appear. It tests HTTP error handling, strict IPv4 validation, code reuse and deterministic sorting.

  PinterestSite Reliability EngineerSoftware Engineering FundamentalsTechnical Screen
• Fewest Buses to Ride Between Two Stops on Looping Bus LinesMedium

  A coding problem about bus lines that loop through fixed lists of stops: find the fewest buses a rider must board to travel from a source stop to a target stop, or report that the target cannot be reached. It tests modeling transfers between lines as a graph and keeping the search efficient when many lines share stops.

  PinterestSoftware EngineerCoding & AlgorithmsOnsite
• Find First Prefix-Matching WordMedium

  This problem tests your ability to efficiently search through a collection of strings to find the first element that satisfies a specific prefix condition, emphasizing clean iteration and edge case management.

  PinterestSoftware EngineerCoding & AlgorithmsTechnical Screen
• Find available reservation time slotsMedium

  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.

  PinterestData ScientistCoding & AlgorithmsOnsite
• Find list pair with maximum overlapMedium

  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.

  PinterestData ScientistCoding & AlgorithmsOnsite
• Find minimum shuttle transfersMedium

  Find minimum shuttle transfers evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  PinterestSoftware EngineerCoding & AlgorithmsOnline Assessment
• Find the Minimum Number of Coins for an AmountMedium

  Tests dynamic programming and breadth-first search techniques to find the minimum number of coin denominations required to reach a target value, including edge cases like zero and unreachable sums.

  PinterestSoftware EngineerCoding & AlgorithmsOnsite
• Find the Share of Power Listeners per Region from Play EventsMedium

  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.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top video category by average timeMedium

  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.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• First Word Matching Each Prefix QueryMedium

  This problem tests your ability to efficiently query a list of strings to find the first occurrence that matches a given prefix, focusing on optimal space-time tradeoffs using tree-based or map-based indexing.

  PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
• Generate Attribute CombinationsMedium

  Explain Cartesian-product generation for attribute choices while preserving mixed value types and making ordering, duplicates, and empty cases explicit.

  PinterestSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Generate all hyperparameter combinationsMedium

  This problem tests your ability to generate the Cartesian product of multiple lists, a fundamental task in grid search for hyperparameter tuning.

  PinterestMachine Learning EngineerCoding & AlgorithmsTechnical Screen