Data Scientist Interview Questions

Showing 1831–1860 of 1971 results

• 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
• UberMedium·Data Scientist
  Evaluate marketplace interventions

  This question tests the ability to design an experimentation framework for a two-sided marketplace, specifically addressing interference between supply and demand and selecting appropriate success metrics.

  Analytics & ExperimentationOnsiteExperimentationMarketplaceCausal Inference
• 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
• UberMedium·Data Scientist
  Find the Next Larger Palindrome

  This question evaluates proficiency in numeric and string manipulation, algorithmic thinking, and edge-case reasoning related to palindromic number generation.

  Coding & AlgorithmsOnsite
• UberMedium·Data Scientist
  Formulate OR model to reduce driver backtracking

  This question evaluates skills in defining a quantitative driver-backtracking metric from GPS and assignment logs, detecting and validating backtracking segments, and formulating constrained optimization models for dispatch and repositioning, drawing on statistics, operations research, and stochastic optimization.

  Statistics & MathOnsite
• 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 FizzBuzz

  This problem tests foundational programming skills by requiring the implementation of conditional logic and string manipulation based on modular arithmetic patterns.

  Coding & AlgorithmsOnsiteAlgorithmsControl FlowStrings
• UberMedium·Data Scientist
  Implement Streaming Clustering for Numbers

  This question evaluates a data scientist's competency in online clustering for numeric streams, testing understanding of streaming algorithms, bounded-memory cluster summaries, online updates, and issues including initialization, convergence behavior, outlier handling, and concept drift.

  Machine LearningOnsite
• 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
• UberHard·Data Scientist
  Investigate ride declines and test free trials

  This question evaluates proficiency in product analytics, cohort and funnel analysis, retention and supply diagnostics, pricing and subscription economics, and experimentation design.

  Analytics & ExperimentationOnsite
• 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
• UpstartEasy·Data Scientist
  Assess Probability of Heads in Coin Tosses

  Assess Probability of Heads in Coin Tosses 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 & MathOnsite
• 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 Expected Streaks in Coin Toss Sequence

  Calculate Expected Streaks in Coin Toss Sequence 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 & MathOnsite
• 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
  Combine noisy thermometers; compute random-walk correlations

  This question evaluates estimation and probabilistic reasoning skills, specifically the construction of unbiased estimators and variance-minimizing linear combinations for combining noisy measurements, together with computation and asymptotic characterization of correlations in a 2D simple random walk.

  Statistics & MathOnsite
• UpstartEasy·Data Scientist
  Compute decay, OLS, and classic probability results

  This question evaluates probability and statistical modeling skills—probabilistic reasoning for exponential decay and the coupon-collector expectation, derivation of population OLS coefficients for simple linear models, simulation implementation, and conditional probability reasoning exemplified by the Monty Hall scenario.

  Statistics & MathOnsite