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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 601–630 of 641 results
- UberMediumData ScientistDesign 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 - UberEasyData ScientistDesign 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 - UberHardData ScientistDesign 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 - UberMediumData ScientistEvaluate 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 - UberMediumData ScientistEvaluate 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 - UberMediumData ScientistFind 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 - UberMediumData ScientistFormulate 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 - UberMediumData ScientistImplement 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 - UberMediumData ScientistImplement 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 - UberHardData ScientistInvestigate 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 - UpstartEasyData ScientistAssess 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 - UpstartEasyData ScientistCalculate 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 - UpstartEasyData ScientistCombine 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 - UpstartEasyData ScientistCompute 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 - UpstartMediumData ScientistDesign Algorithm for Longest Substring with K Distinct Characters
This question evaluates proficiency in string-processing and sliding-window algorithm patterns, assessing competency in designing efficient solutions and managing data structures to track distinct elements and reason about time and space complexity.
Coding & AlgorithmsOnsite - UpstartMediumData ScientistEstimate Family Proportions and Explain Regression Anomalies
Estimate Family Proportions and Explain Regression Anomalies 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 - UpstartMediumData ScientistEstimate and Derive Regression Coefficient for X on y
Estimate and Derive Regression Coefficient for X on y 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 - UpstartEasyData ScientistExplain L1 vs L2 and ridge vs lasso
This question evaluates understanding of regularization techniques in Machine Learning—specifically distinctions between L1 and L2 norms and their instantiation as Lasso and Ridge regression—covering effects on objective functions, parameter sparsity, geometric intuitions, handling of correlated features, and the role of feature scaling.
Machine LearningOnsite - UpstartEasyData ScientistImplement factorial and count trailing zeros
This question evaluates understanding of factorial computation and algorithmic techniques, including iterative versus recursive approaches, handling large integers and recursion constraints, and efficient counting of trailing zeros via number-theoretic reasoning.
Coding & AlgorithmsOnsite - UpstartMediumData ScientistLeverage Existing Model for Low Credit Score Applicants
Leverage Existing Model for Low Credit Score Applicants 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 LearningOnsite - UpstartMediumData ScientistSolve core probability/statistics mini-problems
Category: Statistics & Math; this prompt evaluates core probability and statistical concepts — exponential decay and discrete survival counts, Bayesian updating, properties of population OLS coefficients, conditional-probability reasoning exemplified by Monty Hall, the coupon-collector expected time, and the conceptual distinction between likelihood and probability — relevant for Data Scientist roles. It is commonly asked because it probes foundational distributional intuition, independence and conditional inference, and expectation/estimation at an introductory-to-intermediate theoretical level that underpins applied modeling and experimental interpretation.
Statistics & MathOnsite - Walmart LabsMediumData ScientistAssess Cultural Fit in Informal Hiring Conversations
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit in Informal Hiring Conversations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - Walmart LabsMediumData ScientistFind Index Pairs of Matching Elements in Arrays
This question evaluates the ability to work with sorted arrays, index mapping, duplicate handling, and reasoning about correctness and algorithmic complexity.
Coding & AlgorithmsOnsite - Walmart LabsMediumData ScientistPredict Next-Period Conversion Rate Using Historical Campaign Data
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 Predict Next-Period Conversion Rate Using Historical Campaign Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Walmart LabsMediumData ScientistUnderstand Propensity Score Matching in Business Analytics
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand Propensity Score Matching in Business Analytics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - WaymoMediumData ScientistConservative Wait Estimates Cut Conversion and Pickup Time: Did Efficiency Improve?
A ride service showed riders conservative pickup-time estimates; afterwards conversion fell 5% and actual time to pickup fell 25%. Decide whether fleet efficiency really improved and list alternative explanations, testing selection effects and metric design.
Analytics & ExperimentationOnsite - WaymoEasyData ScientistHow predict vehicles’ turn direction at intersection?
This problem tests the ability to design an end-to-end machine learning pipeline for vehicle intent prediction, focusing on feature engineering, sequence modeling, and temporal validation.
Machine LearningOnsiteTime-SeriesFeature EngineeringAutonomous Driving - WaymoEasyData ScientistHow validate a driving simulation is realistic?
This question tests the ability to design an evaluation framework to compare simulated vehicle behavior against real-world sensor and trajectory logs to ensure high-fidelity simulation.
Analytics & ExperimentationOnsiteSimulationValidationStatistics - WaymoEasyData ScientistImplement nested template string substitution
This problem tests your ability to handle recursive data structures and string parsing by expanding nested placeholders within a template string while identifying potential circular dependencies.
Coding & AlgorithmsOnsiteRecursionString ProcessingMemoization - WaymoEasyData ScientistMerge overlapping intervals per group in pandas
This problem tests your ability to handle temporal data by merging overlapping time intervals within distinct groups using pandas and custom logic.
Data Manipulation (SQL/Python)OnsitePandasData ManipulationIntervals
Uber
Upstart
Walmart Labs
Waymo