Data Scientist Interview Questions

Showing 571–600 of 1971 results

• CoupangMedium·Data Scientist
  Monthly Cohort Retention

  This question tests the ability to transform raw user activity logs into a cohort retention matrix by calculating the time elapsed between a user's first interaction and subsequent activity.

  Data Manipulation (SQL/Python)Technical ScreenSQLRetentionCohort Analysis
• CoupangMedium·Data Scientist
  Predicting the Next Elevator Call Location

  This question tests the ability to design a spatio-temporal forecasting system to predict future elevator call locations, focusing on feature engineering, model selection, and handling data sparsity.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringSpatio-Temporal
• CoupangMedium·Data Scientist
  Reserving an Elevator for Food Deliveries

  This question evaluates your ability to design an A/B test for an operational policy change in a high-traffic environment, focusing on cluster randomization, metric selection, and handling network effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingExperiment DesignMetrics
• DRWMedium·Data Scientist
  Compute Markov steady state and expectations

  Compute Markov steady state and expectations 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
• DRWEasy·Data Scientist
  Derive eigenvalues and sum for inverse matrix

  Derive eigenvalues and sum for inverse matrix 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
• DRWMedium·Data Scientist
  Differentiate sample vs population standard deviation

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Differentiate sample vs population standard deviation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• DRWMedium·Data Scientist
  Solve Markov and distribution expectation problems

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Solve Markov and distribution expectation problems states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• DRWMedium·Data Scientist
  Solve movie ratings, array, release scheduler

  This question tests algorithmic problem-solving across three domains: greedy array optimization, tree-based data structure design, and efficient sequence merging. It evaluates practical coding ability and complexity analysis, commonly used in quantitative and data science interviews to assess how candidates identify inefficiencies and select the right data structure to improve performance.

  Coding & AlgorithmsOnsite
• DRWMedium·Data Scientist
  Solve three algorithmic tasks in Python

  This question evaluates proficiency in dynamic programming, range-query data structures (Fenwick/segment tree), sorting and two-pointer techniques, and algorithmic complexity analysis within the Coding & Algorithms domain for Python-based implementation.

  Coding & AlgorithmsOnsite
• DatabricksHard·Data Scientist
  Explain Linear Regression Assumptions

  This question tests the candidate's mastery of the Gauss-Markov theorem and the consequences of violating Ordinary Least Squares (OLS) assumptions in real-world data science workflows.

  Statistics & MathTechnical ScreenStatisticsRegressionOLS
• DatabricksMedium·Data Scientist
  Explain Linear Regression Feature Transformation Equivalence

  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 Explain Linear Regression Feature Transformation Equivalence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment
• DatabricksHard·Data Scientist
  Find Top-5 Similar Rows

  This question tests the ability to implement a pairwise similarity comparison between rows using a custom distance metric while handling missing data and ranking results.

  Data Manipulation (SQL/Python)Technical ScreenPandasSimilarityData Manipulation
• DatabricksEasy·Data Scientist
  Find top-5 most similar rows across datasets

  This question evaluates understanding and implementation of similarity/distance metrics (e.g., MSE), data preprocessing issues such as handling NULLs and feature scaling, and the ability to perform efficient cross-dataset comparisons and top-k ranking.

  Data Manipulation (SQL/Python)Technical Screen
• DatabricksEasy·Data Scientist
  Relate coefficients under linear feature transformation

  This question tests the relationship between regression coefficients when predictors undergo linear transformations, specifically focusing on scaling and centering transformations.

  Statistics & MathTechnical ScreenLinear RegressionLinear AlgebraFeature Engineering
• DatabricksHard·Data Scientist
  Test coin fairness from 560 tails in 1000 flips

  This problem evaluates hypothesis testing for proportions—formulating null and alternative hypotheses, applying the binomial-to-normal approximation, and interpreting p-values; it belongs to Statistics & Math for Data Scientist roles and is pitched at an introductory/undergraduate applied statistics level.

  Statistics & MathTechnical Screen
• DiscordMedium·Data Scientist
  Count a Character in a String

  Count case-sensitive occurrences of a character in a string, including empty input and repeated matches.

  Coding & AlgorithmsTechnical Screen
• DiscordMedium·Data Scientist
  Design Product Notification and Autocomplete Experiments

  This question evaluates experimental design, causal inference, metric definition, selection bias analysis, logging and privacy considerations, and product analytics competencies for a data scientist working in Analytics & Experimentation.

  Analytics & ExperimentationTechnical Screen
• DiscordMedium·Data Scientist
  Evaluate Model-Targeted Subscription Notifications

  Evaluate subscription-targeting notifications with incrementality metrics, A/A preparation, flat-result diagnosis, and alternative causal designs.

  Analytics & ExperimentationTechnical Screen
• DiscordMedium·Data Scientist
  Explain a Business Project That Used Causal Inference

  Prepare a causal-inference project answer covering the business decision, identification assumptions, uncertainty, and lessons for a repeat study.

  Behavioral & LeadershipTechnical Screen
• DiscordMedium·Data Scientist
  Return Words Present in Exactly One Message

  Find words appearing in exactly one of two messages using set symmetric difference while clarifying tokenization and case rules.

  Coding & AlgorithmsTechnical Screen
• DisneyHard·Data Scientist
  Demonstrate influential product leadership under ambiguity

  This question evaluates a candidate's influential product leadership, stakeholder alignment, metric definition, and analytic judgment under ambiguity, including the ability to prioritize trade-offs and drive decisions without formal authority.

  Behavioral & LeadershipHR Screen
• DisneyHard·Data Scientist
  Diagnose and decide on watch-time drop

  This question evaluates a data scientist's skills in causal inference, experiment analytics, metric definition and guardrails, confounder adjustment, and rapid decision-making under time pressure.

  Analytics & ExperimentationHR Screen
• DisneyMedium·Data Scientist
  Write complex SQL for streaming funnels

  This question evaluates a candidate's ability to write complex ANSI-SQL for event-level funnel analysis, including temporal windowing, user attribution, multi-stage aggregation, and handling of event schemas.

  Data Manipulation (SQL/Python)HR Screen
• DoorDashMedium·Data Scientist
  Analyze Driver Requests for Food Delivery Orders

  This question evaluates data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain, focusing on window functions for ranking and temporal comparison, joins, and aggregation when analyzing orders and driver requests.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashHard·Data Scientist
  Assess Success Criteria for Bike-Courier Delivery Launch

  Evaluates marketplace analytics for launching a bike-courier delivery option in dense delivery markets. Strong answers define stakeholder value, success metrics, market selection, phased rollout, guardrails, and causal evaluation under network effects.

  Analytics & ExperimentationTechnical Screen
• DoorDashMedium·Data Scientist
  Boost App Installs: Analyze and Experiment with Conversion Funnel

  DoorDash analytics prompt on improving mobile web to app-install conversion, covering funnel measurement, attribution, cohorts, install and activation experiments, incentives, guardrails, and long-term retention impact.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Build ETA prediction and simulate impact

  This question evaluates machine learning competencies for ETA prediction, including feature engineering, leakage detection, time-based validation, point and quantile modeling, evaluation and calibration metrics, cost-sensitive decisioning, explainability and fairness auditing, and production deployment considerations.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Build a late-delivery risk model

  This question evaluates competencies in production-ready predictive modeling, including target definition, feature engineering, temporal train/validation splitting to avoid leakage, model evaluation and calibration, monitoring and ramp strategies, and translating probabilistic outputs into business-impacting decisions for a Data Scientist.

  Machine LearningOnsite
• DoorDashMedium·Data Scientist
  Calculate Late Delivery Percentage and Top Customers

  This question evaluates data manipulation and aggregation skills for a Data Scientist role, focusing on date handling, computing the percentage of late orders, and ranking customers using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashEasy·Data Scientist
  Calculate Monthly Restaurant Sales Growth

  This problem tests the ability to aggregate transactional data by month and use window functions to calculate percentage growth between consecutive periods while handling null values.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation