Data Scientist Interview Questions

Showing 631–660 of 1076 results

• MetaEasy·Data Scientist
  How would you define and use retention metrics?

  This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.

  Analytics & ExperimentationTechnical Screen
• MetaEasy·Data Scientist
  How would you design a Shop Ads ranking algorithm?

  This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.

  Machine LearningTechnical ScreenSystem DesignRankingAds
• MetaEasy·Data Scientist
  How would you evaluate pixel-issue notifications?

  This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.

  Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference
• MetaEasy·Data Scientist
  How would you evaluate upranking shop ads?

  This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech
• MetaMedium·Data Scientist
  How would you measure Group Call success?

  This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Identify 3-Person Call Cycles in Video-Calling App

  This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Identify Algorithms for Detecting Malicious Duplicated Content

  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 Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Data Scientist
  Identify non-table data for feature demand

  The question evaluates a data scientist's competency in triangulating product demand from non-table sources, defining and de-biasing signals, specifying metrics and guardrails, and designing evidence-driven experiments within the Analytics & Experimentation domain for a Data Scientist role.

  Analytics & ExperimentationTechnical Screen
• MetaEasy·Data Scientist
  Investigate why an advertiser’s spend decreased

  This question tests your ability to systematically isolate the root cause of a drop in advertising spend by decomposing metrics and evaluating external versus internal factors.

  Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics DecompositionAdTech
• MetaMedium·Data Scientist
  Measure Harmful Content Impact with Key Metrics

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Measure Harmful Content Impact with Key Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Measure and mitigate notification spam

  This question tests the ability to design an experimentation framework for notification systems while accounting for user fatigue, long-term ecosystem health, and signal interference.

  Analytics & ExperimentationTechnical ScreenExperimentationProduct MetricsCausal Inference
• MetaMedium·Data Scientist
  Model Unique Recipients with Poisson Distribution and Test Fit

  Evaluates statistical modeling of unique recipient counts per caller using count distributions. Strong answers compare Poisson, binomial, negative binomial, zero-inflated, and segmented models, then test fit with dispersion checks, likelihood metrics, chi-square tests, and simulation-based diagnostics.

  Statistics & MathTechnical Screen
• MetaMedium·Data Scientist
  Optimize Travel Costs and Generate Rotational Symmetric Numbers

  This question evaluates algorithmic optimization and complexity-analysis skills by combining array-based time-dependent pricing for round-trip cost minimization with combinatorial generation of rotationally symmetric (strobogrammatic) k-digit numbers.

  Coding & AlgorithmsTechnical Screen
• MetaHard·Data Scientist
  Persuade engineers to launch pinned-unread chats

  This question evaluates persuasion, product sense, metrics-driven reasoning, and technical trade-off analysis for a Data Scientist within a Behavioral & Leadership domain, requiring clear problem definition, baseline metrics, impact estimation, and consideration of engineering constraints like ranking complexity, real-time updates, caching, client performance, edge cases, and accessibility. It is commonly asked to assess the ability to influence engineering stakeholders, design incremental rollouts with success metrics and guardrails, and balance high-level conceptual strategy with practical implementation planning (both conceptual understanding and practical application).

  Behavioral & LeadershipTechnical Screen
• MetaHard·Data Scientist
  Prove friends outperform unconnected; design metrics, observational analysis, and rollout experiment

  A Meta data-scientist technical-screen question on Analytics & Experimentation: using only info_stream_views and post_reactions, prove whether Friend-authored content is 'more social' than Unconnected content. It tests denominator-complete metric design with relationship attribution, observational causal validation (fixed effects + propensity/AIPW), and a network-aware rollout experiment with power, CUPED, interference mitigations, and guardrails — plus quantifying the long-term and discovery value of Unconnected content beyond near-term engagement.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Prove high-quality pixels improve ad performance

  This question tests your ability to design a rigorous causal experiment to validate a product hypothesis while accounting for selection bias, confounding variables, and operational constraints.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperiment DesignMetric Strategy
• MetaMedium·Data Scientist
  Resolve Team Conflicts and Exceed Job Expectations Successfully

  Evaluates behavioral interview readiness for initiative beyond formal responsibilities and team conflict resolution. Strong answers use STAR, explain why the candidate stepped in, show stakeholder management, quantify impact, and resolve conflict without blame.

  Behavioral & LeadershipTechnical Screen
• MetaMedium·Data Scientist
  Select and prioritize metrics with guardrails

  This question evaluates a candidate's ability to design a robust metrics framework for product experiments, covering selection and normalization of success metrics, feature-only sizing and ecosystem metrics, explicit cannibalization reads, guardrails, multiple-testing control, and back-of-the-envelope LTV/ARPDAU impact estimation.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Should We Launch Group Calling?

  A Meta Data Scientist technical-screen case: using only one-to-one call logs, decide whether to build a group calling feature and how to measure a launch's success. It tests product analytics, proxy-metric design, network analysis, causal inference, and experiment design under social-network interference.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Track Success and Guardrail Metrics for Push Notifications

  Evaluates success and guardrail metrics for a push-notification feature in a networked travel app. Strong answers measure incremental itinerary engagement and retention, monitor opt-outs and fatigue, and design a cluster or graph-aware experiment to reduce spillovers.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Uncover User Needs for Group Calling Effectively

  Meta product analytics prompt on group calling, covering user-needs surveys, competitor benchmarking without direct data, experiment design, network effects, contamination, metrics, and guardrails.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  What features and feature selection would you use?

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Which clustering algorithm would you use and why

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Write SQL for CTR and Revenue

  This question tests the ability to join event logs with reference data, perform conditional aggregation to calculate click-through rates (CTR), and apply date-sensitive currency conversions.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationJoin
• MetaMedium·Data Scientist
  Write SQL for Pixel Signal Metrics

  This question evaluates SQL-based data manipulation and analytical competencies, specifically joining and aggregating event and ad performance tables, computing event-weighted metrics and composite data-quality scores (such as validity and match-rate based measures) across a 30-day window.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for daily chats and fast replies

  This question evaluates a candidate's ability to manipulate relational data and reason about temporal event sequences, specifically testing skills in SQL aggregation, distinct counting, deduplication, bidirectional relationship handling, and time-windowed event matching.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for multi-account metrics

  This question tests your ability to perform complex aggregations across multiple tables, specifically using conditional logic and joins to compute user engagement metrics.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for seller and category metrics

  This question tests the ability to perform complex aggregations, handle date filtering, and calculate performance ratios while managing potential division-by-zero errors in a multi-table SQL environment.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for social feed metrics and ties

  This question evaluates advanced SQL analytics skills and competency in data modeling for social feed metrics, including relationship joins, viewer-day temporal aggregation, weighted interaction scoring, and per-user top-N ranking.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL to infer group-call demand

  This question evaluates SQL-based data manipulation and product-analytics competencies, including temporal sessionization, graph connectivity inference for detecting overlapping 1:1 call loops, deduplication trade-offs (UNION vs UNION ALL), and computation of derived metrics to estimate latent feature demand within the Data Manipulation (SQL/Python) domain. It is commonly asked because it measures practical application of event-time windowing, connected-component reasoning and edge-case handling (e.g., excluding failed connections) for producing daily summary metrics, emphasizing hands-on SQL proficiency over purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen