Data Scientist Interview Questions

Showing 541–570 of 1076 results

• LyftMedium·Data Scientist
  Compute Commuter Ride Probabilities

  This question tests the ability to apply Bayes' Theorem and marginal probability to model commuter behavior and predict ride-hailing demand based on environmental factors.

  Statistics & MathTechnical ScreenProbabilityBayesian InferenceConditional Probability
• LyftEasy·Data Scientist
  Determine Probability of Single Ride on Following Day

  Evaluates a binomial probability setup for a ride-pricing experiment with two independent offers. Strong answers model the number of reasonable prices as Binomial(2, P), compute exactly one success as 2P(1-P), and compute permanent-rider probability as P squared.

  Statistics & MathTechnical Screen
• LyftHard·Data Scientist
  Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

  Evaluates dynamic-pricing system design for Lyft's two-sided marketplace. Strong answers separate prediction from control, include real-time demand and supply features, model elasticity and ETA response, optimize with fairness and stability constraints, and validate through simulation and experiments.

  Machine LearningTechnical Screen
• LyftEasy·Data Scientist
  Explain Bayes’ Theorem and P-Value in Decision-Making

  Explain Bayes’ Theorem and P-Value in Decision-Making evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• LyftHard·Data Scientist
  How to Target Coupon Users

  This question evaluates a data scientist's competency in causal inference, uplift and propensity modeling, user segmentation, and experimental design for targeted marketing campaigns.

  Analytics & ExperimentationTechnical Screen
• LyftHard·Data Scientist
  How to target commute coupon users?

  This question tests your ability to design an experiment to measure the incremental lift of a coupon program and your strategy for segmenting users based on commuting behaviors to maximize ROI.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationUser Segmentation
• LyftMedium·Data Scientist
  Investigate Metric Drops and Coupon Retention

  This question tests your ability to diagnose a sudden drop in a core business metric, distinguish between technical tracking issues and product-driven behavior, and design a strategy to evaluate coupon effectiveness.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsExperimentationCausal Inference
• LyftMedium·Data Scientist
  Investigate Sudden Metric Changes and Design A/B Test

  Investigate Sudden Metric Changes and Design A/B Test evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• LyftMedium·Data Scientist
  Investigate a 7% Monthly Active Riders Drop and a 20% Wait-Time Increase

  Tests your ability to conduct a structured root-cause analysis on simultaneous marketplace metric regressions, decomposing supply-demand dynamics and separating correlation from causation.

  Analytics & ExperimentationTechnical ScreenMarketplace AnalyticsRoot Cause AnalysisMetrics & KPIs
• MetaMedium·Data Scientist
  Analyze Bookstore Data to Identify Top Payment Methods

  This question evaluates data manipulation and analytical SQL/Python competency, focusing on relational joins, aggregations, string filtering, ranking, and proportion calculations across multiple tables in a bookstore schema.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Mobile Promo Orders with SQL Query and Metrics

  This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Seller Activity and Vehicle Listing Interactions

  This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Social Media Engagement with SQL Queries

  This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Top Call Initiators and Active French Video Callers

  This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze User Engagement and Spammer Read-Rate in SQL

  This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyzing abuse in the content‑reporting system

  Evaluates reporting-system analytics for valid reports and misuse detection in moderation workflows. Strong answers define distinct reported-user share, write SQL for valid reports, and detect abusive reporters through frequency, hit-rate, burst, and coordination metrics.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Assess ranking change and design experiment

  This question evaluates a candidate's competency in causal inference, experimentation design, metric definition, and observational analytics for product ranking and engagement problems within the Analytics & Experimentation domain for Data Scientist roles.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Assessing whether a new metric A is meaningful for News Feed

  Evaluates whether a proposed News Feed proxy metric is meaningful for long-term user value. Strong answers test predictive power, actionability, gaming risk, true-outcome alignment, collection cost, privacy, latency, and decision criteria.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Brainstorm how to optimize email engagement

  This question evaluates a data scientist's competency in lifecycle email optimization, attribution of incremental on-site engagement, experiment design, metric selection, and trade-offs involving deliverability and guardrails within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Calculate CTR and ad revenue

  This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationMetrics
• MetaMedium·Data Scientist
  Calculate Daily Survey Response Rates by Country

  Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLData Aggregation
• MetaMedium·Data Scientist
  Calculate French User Engagement and U.S. Call Duration

  This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Survey Response Rate and Quality Metric in SQL

  This question evaluates SQL data-manipulation competencies such as aggregation, NULL handling, deduplication (first-per-group), and computing engagement and quality metrics from event logs.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Video Call Usage Metrics by Country and Date

  This question evaluates data manipulation and product-analytics skills, focusing on joining user and call datasets, aggregations for distinct-user percentages, and per-DAU duration calculations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaHard·Data Scientist
  Choose group-call size cap via experiment

  This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Clarify scope and align to mission

  This question evaluates a data scientist's product sense, leadership and stakeholder-alignment skills, including scope clarification, mission translation, metric definition, and collaborative communication.

  Behavioral & LeadershipTechnical Screen
• MetaHard·Data Scientist
  Communicate trade-offs and influence launch

  This question evaluates product judgment, experimentation interpretation, and cross-functional leadership skills for a data scientist, focusing on communicating trade-offs between per-account metric lifts and cross-account cannibalization; Category: Behavioral & Leadership, Domain: data science and product experimentation.

  Behavioral & LeadershipTechnical Screen
• MetaMedium·Data Scientist
  Compare Survey Satisfaction for New and Established Users

  Tests your ability to write PostgreSQL queries that segment users into cohorts based on date logic, filter invalid survey events, and calculate aggregate satisfaction metrics.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqldata-aggregation
• MetaMedium·Data Scientist
  Compute 7-day views and reactions by relationship

  This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute CTR overall and by campaign type

  This question evaluates data manipulation and analytics skills, specifically SQL aggregations, deduplication of event logs, time-window filtering, joins to campaign metadata, and safe ratio computation including division-by-zero handling.

  Data Manipulation (SQL/Python)Technical Screen