Data Scientist Interview Questions

Showing 931–960 of 1971 results

• LinkedInHard·Data Scientist
  Measure Causal Impact of Self-Selected App Redesign

  LinkedIn causal inference prompt on measuring a self-selected app redesign, covering potential outcomes, ATT, propensity matching or weighting, staggered difference-in-differences, event studies, covariates, balance, and pre-trend validation.

  Statistics & MathOnsite
• LinkedInHard·Data Scientist
  Measure Relevant Feed Success

  This question tests the ability to define North Star metrics for a social feed and design an experimentation framework that accounts for long-term ecosystem health and cross-side network effects.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsA/B TestingMetrics
• LinkedInMedium·Data Scientist
  Measure Success of New B2B Product

  Evaluates success measurement for a new LinkedIn B2B SaaS product at account and seat levels. Strong answers define activation, engagement, value, retention, expansion, anomaly analysis, and growth prioritization.

  Analytics & ExperimentationOnsite
• LinkedInMedium·Data Scientist
  Present Insights from Course Supply and Participation Data

  Analyze course catalog supply and learner participation using time trends, category comparisons, utilization measures, and evidence-based presentation.

  Analytics & ExperimentationOnsite
• LinkedInMedium·Data Scientist
  Resolve Conflicting A/B Test Results in Cities

  Evaluates interpretation of conflicting A/B test results across cities using Simpson's paradox and stratified estimands. Strong answers check weighting, SRM, heterogeneity, confidence intervals, and rollout criteria.

  Analytics & ExperimentationTechnical Screen
• LinkedInEasy·Data Scientist
  Resolve Simpson’s paradox in A/B email test

  This question tests your ability to identify and resolve Simpson's Paradox in A/B test results, where an aggregate trend disappears or reverses when segmented by user cohorts.

  Analytics & ExperimentationTechnical ScreenA/B TestingSimpson's ParadoxConfounding
• LinkedInMedium·Data Scientist
  Single Queue vs Multiple Queues — Service Design

  Evaluates queueing-system reasoning for single-line versus multiple-line bank teller designs. Strong answers compare M/M/c pooling with parallel M/M/1 queues, waiting-time averages, variance, stability, fairness, and operations.

  Analytics & ExperimentationOnsite
• LinkedInMedium·Data Scientist
  Test If Initial Video Uploads Are Shorter Than Later Ones

  Evaluates hypothesis testing for whether users' first video uploads are shorter than later uploads. Strong answers define a clean data slice, paired user-level metrics, robust tests, and SQL-style implementation.

  Analytics & ExperimentationTechnical Screen
• LinkedInMedium·Data Scientist
  Test a New Feature at Its Natural Product Placement

  Design a feature launch experiment that separates natural-placement availability, discoverability, promotional exposure, and downstream product value.

  Analytics & ExperimentationOnsite
• LinkedInMedium·Data Scientist
  [SQL] Job Ad Metrics with Applicant Filter

  This question evaluates proficiency in SQL data manipulation and deduplication, focusing on aggregations, distinct counts, and conditional filtering of event records (views vs applies).

  Data Manipulation (SQL/Python)Technical Screen
• LyftMedium·Data Scientist
  Analyze Rider Behavior in Dynamic-Pricing Trial

  Analyze Rider Behavior in Dynamic-Pricing Trial 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
• LyftMedium·Data Scientist
  Assess Cultural Fit Through Behavioral Interview Questions

  Assess Cultural Fit Through Behavioral Interview Questions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• LyftMedium·Data Scientist
  Commuter Ride-Choice Probability: Committed and Permanent Riders

  Tests discrete probability modeling, complementary counting, and Bayesian updating to analyze multi-day commuter behavior across distinct rider segments.

  Statistics & MathTechnical Screenprobabilitybayes-theoremconditional-probability
• 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
• LyftMedium·Data Scientist
  Compute Poisson supply–demand match probability

  This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.

  Statistics & MathOnsite
• LyftMedium·Data Scientist
  Demonstrate leadership under ambiguity

  This question evaluates leadership, stakeholder communication, ambiguity management, and negotiation competencies for a Data Scientist role, covering problem scoping, assumption documentation, rapid validation, cross-cultural interactions, and data-backed compensation discussions.

  Behavioral & LeadershipOnsite
• LyftMedium·Data Scientist
  Design experiments for marketplace balance

  This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, statistical power calculation, and operational rollout planning for two-sided marketplaces, and it is commonly asked to assess the ability to account for interference, supply constraints, spillovers, and contamination when measuring treatment effects. Categorized under Analytics & Experimentation, it tests both conceptual understanding and practical application by probing exposure and eligibility definitions, metric and guardrail specification, sample-size and clustering adjustments, recognition of heterogeneous treatment effects and sequential monitoring concerns, variance-reduction considerations, and rollout and fallback decision-making.

  Analytics & ExperimentationOnsite
• 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 Causes of Driver WOW Score Drop

  Investigate Causes of Driver WOW Score Drop 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 & ExperimentationOnsite
• 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
• LyftMedium·Data Scientist
  Optimize Driver Repositioning for Minimal Pickup Time

  This question evaluates spatial algorithms and real-time optimization for ride dispatch together with interval scheduling and capacity planning competencies, covering skills in computational geometry, routing, demand forecasting, and temporal overlap reasoning.

  Coding & AlgorithmsOnsite
• LyftMedium·Data Scientist
  Query and transform marketplace data in SQL/Python

  This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.

  Data Manipulation (SQL/Python)Onsite
• MetaHard·Data Scientist
  Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

  Analyze Algorithm's Impact on Diverse Demographics and Validate Causes 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 & ExperimentationOnsite
• 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