Data Scientist Interview Questions

Showing 991–1020 of 1076 results

• 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
• UberMedium·Data Scientist
  Evaluate business value of lower ETA

  This question tests the ability to design an experiment that isolates the causal impact of reduced ETA on user retention and to translate these metrics into business value.

  Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMarketplace Analytics
• UberHard·Data Scientist
  Evaluate impact without randomized experiments

  This question evaluates a data scientist's competence in causal inference and identification strategies for estimating promotion effects from observational longitudinal data, emphasizing challenges such as staggered rollout, interference (spillovers), seasonality, and uncertainty quantification.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Explain and validate A/B test assumptions

  This question evaluates proficiency in experimental design, causal inference, and statistical diagnostics for A/B testing, covering competencies such as randomization integrity, SUTVA/interference, noncompliance, time-varying effects, metric missingness, sequential testing, and heterogeneous treatment effects.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Formulate hypotheses and compute AB test significance

  This question evaluates a data scientist's competency in experimental design and statistical inference for A/B testing, covering hypothesis formulation, difference-in-proportions testing and confidence intervals, guardrail analysis, multiple-testing correction, interim alpha-spending approaches, and variance-reduction techniques such as CUPED.

  Statistics & MathTechnical Screen
• UberEasy·Data Scientist
  How to experiment on ETA reduction

  This problem tests the ability to design an A/B test for product improvements and address potential confounding factors like selection bias and spillover effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMarketplace Dynamics
• UberMedium·Data Scientist
  How would you build UberEats ranking?

  This question assesses the ability to design an end-to-end ranking system for a food delivery platform, focusing on objective formulation, feature selection, and the trade-offs between relevance and business constraints.

  Machine LearningTechnical ScreenRecommender SystemsLearning to RankFeature Engineering
• UberEasy·Data Scientist
  How would you design an ETA prediction system?

  This problem tests the ability to design an end-to-end machine learning system for predicting ride arrival times, focusing on feature selection, model architecture, and evaluation strategies.

  Machine LearningTechnical ScreenML System DesignRegressionFeature Engineering
• UberMedium·Data Scientist
  Implement weighted sampling without replacement

  This question evaluates a candidate's ability to design numerically stable, streaming weighted-sampling methods and tests competencies in randomized algorithms, probability, numerical analysis, and memory/time trade-offs.

  Coding & AlgorithmsTechnical Screen
• UberHard·Data Scientist
  Improve Estimated Time of Arrival for Uber Riders

  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 Improve Estimated Time of Arrival for Uber Riders states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UberEasy·Data Scientist
  Measure feature impact with switchback, PSM, and CACE

  This question evaluates proficiency in causal inference and experimental design for product metrics, specifically testing knowledge of switchback experiments, time-series adjustments (seasonality and autocorrelation), propensity score methods for observational comparisons, and complier-average causal effect (LATE/CACE) estimation; it belongs to the Analytics & Experimentation and Data Science domain. It is commonly asked because it probes handling of real-world complications—time and city heterogeneity, non-compliance, and derived-metric inference—and tests both conceptual understanding of causal assumptions and practical application of statistical adjustment and inference.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Measure rider incentive causal ROI

  This question evaluates a data scientist's competency in causal inference and experimental design, marketplace health metric construction, ROI estimation, and the handling of selection bias, spillover effects, multiple testing, and heterogeneity in two-sided platforms.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Move zeros to front

  This problem tests your ability to manipulate array indices in-place to reorder elements according to a specific condition while maintaining relative stability.

  Coding & AlgorithmsTechnical ScreenArraysTwo-PointersIn-place
• UberHard·Data Scientist
  Navigate urgency, priorities, and conflict

  This question evaluates a candidate's ability to manage ambiguity, prioritize competing projects and deadlines, navigate cross-team dependencies, influence stakeholders without formal authority, and quantify outcomes and process improvements.

  Behavioral & LeadershipTechnical Screen
• UberMedium·Data Scientist
  Should Uber double member discounts?

  This problem tests your ability to design a controlled experiment for a two-sided marketplace and evaluate whether a discount-doubling strategy is statistically and economically viable.

  Statistics & MathTechnical ScreenA/B TestingCausal InferenceMarketplace Economics
• UberMedium·Data Scientist
  Write SQL and Pandas for Uber Trips

  This question evaluates SQL and Pandas data manipulation competencies, including joins, time-based filtering, handling NULLs, aggregations, rate and ratio calculations, and windowed comparisons over grouped city-day data.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Write SQL/Python for CTR analytics

  This question evaluates proficiency in data manipulation and analytics, covering SQL windowing and deduplication for time‑windowed cohort metrics (48‑hour unique CTR) as well as practical Python model evaluation tasks such as precision‑recall plotting, weighted F1 threshold selection, and precision@top‑k.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartMedium·Data Scientist
  Analyze HT vs HH stopping-time probabilities

  This question evaluates understanding of discrete probability, stopping-time analysis, pattern occurrence in Bernoulli sequences, derivation of PMFs and expectations, and the ability to generalize results to biased coins.

  Statistics & MathTechnical Screen
• UpstartMedium·Data Scientist
  Calculate Average Event Value by User ID

  This question evaluates a candidate's skill in group-wise aggregation and data transformation on JSON-like event streams, specifically computing per-user average metrics and producing ranked results.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartEasy·Data Scientist
  Calculate Particle Survival Probability After Time t

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Particle Survival Probability After Time t states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• UpstartMedium·Data Scientist
  Calculate User Revenue and Session Duration in Python

  This question evaluates proficiency in data manipulation and time-series event processing in Python, focusing on per-user revenue aggregation and sessionization to compute average session duration.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartEasy·Data Scientist
  Correct length-biased sampling from family-size survey

  This question evaluates understanding of length-biased sampling and inference under sampling bias, specifically how sampling by individuals (children) differs from sampling by households when estimating family-size probabilities.

  Statistics & MathTechnical Screen
• UpstartMedium·Data Scientist
  Design Push-Notification System for Airport Surge Pricing

  Design Push-Notification System for Airport Surge Pricing evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• UpstartHard·Data Scientist
  Design a Real-Time Personalized Ad Selection System

  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 Design a Real-Time Personalized Ad Selection System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• UpstartEasy·Data Scientist
  Estimate impact without experiments and pick variant

  This question evaluates causal inference and experimental-analysis competencies in Analytics & Experimentation and Data Science, covering observational estimands, causal identification assumptions and biases, uncertainty quantification for A/B/C tests, multiple-comparisons reasoning, and post-launch forecasting and monitoring.

  Analytics & ExperimentationTechnical Screen
• UpstartHard·Data Scientist
  Evaluate channels and allocate budget

  This question evaluates competency in marketing analytics and experimentation for a Data Scientist role, covering funnel analysis, conversion and CAC metrics, attribution modeling, response-curve-based budget allocation, and incremental test design using channel-level spend and outcome data.

  Analytics & ExperimentationTechnical Screen
• UpstartHard·Data Scientist
  Explain Treatment Results and Recommend Launch Criteria for Experiments

  Explain Treatment Results and Recommend Launch Criteria for Experiments 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
• UpstartHard·Data Scientist
  Explain career moves and defend moat

  This question evaluates a candidate's ability to narrate career progression with quantified outcomes, analyze and defend a company's sustainable competitive advantage (moat) using a strategic framework, and present high-visibility decisions and trade-offs under deadline, testing competencies in self-awareness, strategic reasoning, risk assessment, stakeholder communication, and metrics-driven impact reporting for a Data Scientist role in the Behavioral & Leadership category. It is commonly asked to assess how well a candidate synthesizes narrative and quantitative evidence for leadership and cross-functional contexts, and it targets both conceptual understanding of strategic frameworks and practical application of metrics and presentation trade-offs.

  Behavioral & LeadershipTechnical Screen
• UpstartHard·Data Scientist
  Explain tackling ambiguity and defending a decision

  This question evaluates judgment under ambiguity, data-driven decision-making, stakeholder alignment, trade-off analysis, and post-decision learning in a data science context, and is categorized within Behavioral & Leadership interview topics.

  Behavioral & LeadershipTechnical Screen