Data Scientist Interview Questions

Showing 1801–1830 of 1971 results

• UberMedium·Data Scientist
  Build cold-start restaurant ratings

  This problem tests the ability to design an end-to-end machine learning system for predicting restaurant ratings when historical user-item interactions are scarce, focusing on feature engineering, model selection, and bias mitigation.

  Machine LearningTechnical ScreenCold-StartRegressionFeature Engineering
• UberMedium·Data Scientist
  Can one car serve all riders?

  This problem tests your ability to determine if a single resource (a car) can accommodate a series of scheduled tasks without overlap, requiring efficient sorting and comparison techniques.

  Coding & AlgorithmsTechnical ScreenSortingIntervalsGreedy
• UberMedium·Data Scientist
  Compare Two Coin Proportions

  This question evaluates proficiency in statistical inference for proportions, including hypothesis formulation, comparison of two independent samples, confidence interval interpretation, and interpretation of p-values and sampling variability.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Compute A/B sample size under clustering

  This question evaluates competency in experimental design and statistical inference for A/B testing, specifically sample size calculation for two-sample proportions with unequal allocation, clustering effects (ICC and design effect), and adjustments for bot removal and data-quality attrition.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Compute CDF from a PDF Function

  This problem tests your understanding of numerical integration techniques (specifically the trapezoidal rule) and your ability to implement a functional mapping of a continuous probability density function (PDF) into a discrete cumulative distribution function (CDF).

  Coding & AlgorithmsTechnical ScreenNumerical MethodsProbabilityIntegration
• UberMedium·Data Scientist
  Compute ETA shift and conversion uplift

  This question evaluates proficiency in SQL and Python for time‑series data manipulation and experimental analysis, covering window functions (7‑day percentile_cont medians), date spines and timezone‑aware timestamp truncation, conversion-rate aggregation, difference‑in‑differences uplift estimation, and CUPED adjustment with cluster‑robust standard errors. It is commonly asked to assess practical data engineering and applied statistics skills in the Data Manipulation (SQL/Python) domain, testing applied implementation and causal inference ability rather than only conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Compute maximum concurrent trips from intervals

  This question evaluates a candidate's skill in designing efficient algorithms for interval overlap counting, including event ordering, handling half-open interval edge cases, and reasoning about concurrent activity timing.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Compute square root to 1 decimal

  This question evaluates a candidate's understanding of numerical methods, floating-point precision, and implementation of approximate functions when producing a square root rounded to one decimal place.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Convert a PDF to a CDF

  This question evaluates understanding of probability theory, numerical methods, and algorithmic implementation for converting probability density functions into cumulative distribution functions, addressing analytic versus sampled PDFs along with support, normalization, and monotonicity requirements.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Derive a CDF from a PDF

  This question tests the ability to derive a Cumulative Distribution Function (CDF) from a given Probability Density Function (PDF), ensuring proper integration and boundary conditions.

  Statistics & MathTechnical ScreenProbabilityCalculusStatistics
• UberMedium·Data Scientist
  Describe Conflict and Impact

  This question set evaluates leadership, communication, stakeholder management, decision-making under ambiguity, and technical project ownership for a data scientist, including the ability to quantify impact and reflect on failures.

  Behavioral & LeadershipOnsite
• UberMedium·Data Scientist
  Describe ownership and failure

  This question evaluates ownership, judgment, self-awareness, and the ability to communicate technical depth to non-experts and senior stakeholders, while also probing data-science-specific competencies such as forecasting, experimentation, causal inference, and measuring impact.

  Behavioral & LeadershipOnsite
• UberHard·Data Scientist
  Design ETA prediction for Uber rides

  This question evaluates competency in end-to-end machine learning system design for real-time spatiotemporal ETA prediction, encompassing feature engineering from heterogeneous data sources, uncertainty quantification, model selection, and production serving and monitoring.

  Machine LearningTechnical Screen
• UberMedium·Data Scientist
  Design Pricing Model Experiment

  This question evaluates experimental design and causal inference competencies for marketplace pricing, covering metric selection, guardrail definition, interference and switchback designs, and sample-size and treatment-window tradeoffs.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Design Rideshare Marketplace Causal Analyses

  This question evaluates a data scientist's skills in experimental design and causal inference, marketplace metrics and diagnostics, fairness and safety impact assessment, incentive and policy evaluation, and ETA modeling and forecast quality measurement.

  Analytics & ExperimentationOnsite
• UberMedium·Data Scientist
  Design a Ride-Hailing ETA System

  This question evaluates a data scientist's competency in end-to-end machine learning system design for spatiotemporal prediction, covering temporal and geospatial feature engineering, model family selection, metric tradeoffs and calibration, selection bias and noise handling, latency and deployment constraints, monitoring, and experiment design.

  Machine LearningTechnical Screen
• UberMedium·Data Scientist
  Design airport dispatch with ETA uncertainty

  This question evaluates proficiency in online algorithm design under uncertainty, stochastic modeling of ETAs, multi-objective optimization for resource allocation, and streaming data handling relevant to dispatch systems.

  Coding & AlgorithmsTechnical Screen
• UberHard·Data Scientist
  Design an A/B test; choose Z vs T

  This question evaluates a candidate's competency in experimental design and statistical inference, covering sample-size and power calculations, the choice between Z and T tests, sequential testing/alpha-spending strategies, and causal-inference planning for non-randomized rollouts.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Design an ETA experiment under interference

  This question evaluates a data scientist's competency in experimental design and causal inference for two-sided marketplaces, focusing on interference, partial compliance, metric definition, randomization strategy, instrumentation, estimation, and power calculations within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Design an experiment with marketplace network effects

  This question evaluates a candidate's competency in causal experiment design for networked two‑sided marketplaces, focusing on interference and spillover management, cluster randomization choices, metric definition, power/sample‑size calculations, bias‑reduction approaches, and diagnostic checks.

  Analytics & ExperimentationTechnical Screen
• UberEasy·Data Scientist
  Design and Test a New Feature

  This question evaluates a candidate's ability to design a new product feature for a ride-hailing platform and to translate product goals into measurable outcomes, testing competencies in product sense, metric definition, experiment and causal-inference design, segmentation, and two-sided marketplace trade-off analysis.

  Analytics & ExperimentationOnsite
• UberHard·Data Scientist
  Design and power an incentive experiment

  This question evaluates experimental design, causal inference, A/B testing and statistical power calculation skills, including defining eligibility, unit of randomization, treatment arms, stratification, metric selection with guardrails, handling noncompliance and interference, and interpreting heterogeneous treatment effects.

  Analytics & ExperimentationOnsite
• UberHard·Data Scientist
  Design promo experiment and explain correlation

  This problem tests your ability to distinguish correlation from causation in observational data and to design a robust A/B test to measure the incremental impact of a new rider promotion.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingExperimentation
• UberMedium·Data Scientist
  Determine Sample Size for Promotion Campaign A/B Test

  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 Determine Sample Size for Promotion Campaign A/B Test states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate ETA Impact on Conversion

  This question evaluates a data scientist's skills in causal inference, experimental design, and marketplace analytics, including understanding of confounding, selection bias, interference, and the distinction between correlation and causation.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate Marketplace Changes

  This problem tests your ability to design an A/B test for a two-sided marketplace, focusing on network effects, metric selection, and identifying potential interference between treatment and control groups.

  Analytics & ExperimentationOnsiteExperimentationA/B TestingMarketplace
• UberMedium·Data Scientist
  Evaluate Promotions for Uber Eats Users

  This question tests the ability to design an A/B test for promotions in a two-sided marketplace, focusing on identifying interference (network effects) and selecting appropriate metrics to measure causal impact.

  Machine LearningTechnical ScreenCausal InferenceA/B TestingMarketplace Dynamics
• UberMedium·Data Scientist
  Evaluate Rider-Incentive Program Impact with Key Metrics

  Uber data scientist analytics prompt on evaluating a rider-incentive program, covering experiment design, rider and driver metrics, matching quality, ROI, marketplace externalities, spillovers, and guardrails.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate UberEATS priority delivery and membership

  This question tests the ability to design an experiment for a marketplace feature and evaluate its impact on key business metrics like conversion, frequency, and total contribution margin.

  Analytics & ExperimentationTechnical ScreenExperimentationMarketplaceCausal Inference
• UberMedium·Data Scientist
  Evaluate a cold-start rating launch

  This question tests the ability to design an evaluation framework for a new feature in a two-sided marketplace while accounting for spillover effects and metrics that represent different stakeholder interests.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference