Data Scientist Interview Questions

Showing 961–990 of 1076 results

• Two SigmaMedium·Data Scientist
  Return sorted values after quadratic transform

  This problem tests your ability to efficiently transform elements in a sorted array using a quadratic function and merge them back into a single sorted array in linear time.

  Coding & AlgorithmsTechnical ScreenArraysTwo PointersSorting
• Two SigmaHard·Data Scientist
  Slopes of y-on-x vs x-on-y Regression: Relationship and When They Are Equal

  This question evaluates understanding of simple OLS linear regression estimators, moment-based expressions for slopes, the relationship between y-on-x and x-on-y fitted slopes, and how correlation and variable scaling influence coefficient values.

  Machine LearningTechnical Screen
• Two SigmaMedium·Data Scientist
  Why Is the Sample Mean Approximately Normal for Large Samples?

  This question evaluates understanding of the Central Limit Theorem and asymptotic statistical reasoning, including recognition that the standardized sample mean (equivalently the standardized sum) becomes approximately normal and how that fact underpins inference such as confidence intervals.

  Machine LearningTechnical Screen
• Two SigmaHard·Data Scientist
  Why Use a t-Statistic?

  This problem tests your conceptual understanding of the t-statistic, specifically how sample parameters influence its value and how it bridges the gap between estimation and hypothesis testing.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingQuantitative Finance
• Two SigmaEasy·Data Scientist
  Why is the t-statistic useful

  This question tests the candidate's conceptual understanding of the t-statistic, specifically its role in hypothesis testing when population parameters are unknown.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingInference
• Two SigmaHard·Data Scientist
  Why use the t-statistic?

  This question tests the conceptual understanding of the t-statistic, specifically why it is used instead of the Z-statistic when population parameters are unknown and how it accounts for uncertainty in sample variance.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingT-Distribution
• UberMedium·Data Scientist
  Analyze Cancellation Change with Statistics

  This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Apply instrumental variables under interference

  This question evaluates understanding of causal inference with instrumental variables in the presence of interference, testing skills in defining units and aggregation levels for market‑level spillovers, articulating IV assumptions (relevance, exclusion restriction, independence, monotonicity), and formulating estimation frameworks such as two‑stage least squares. Commonly asked in Statistics & Math interviews for data scientist roles because networked marketplaces invalidate simple A/B tests, it sits in the econometrics/causal inference domain and primarily assesses practical application of IV methods while requiring conceptual understanding of identification, robustness diagnostics, and sensitivity analysis.

  Statistics & MathTechnical Screen
• 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
• 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 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
• 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 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