Data Scientist Interview Questions

Showing 301–330 of 1076 results

• CitadelMedium·Data Scientist
  Explain factor leakage checks and IC/ICIR filtering

  This question tests the conceptual understanding of signal quality metrics and the practical steps required to prevent look-ahead bias and overfitting when developing quantitative trading factors.

  Machine LearningTechnical ScreenQuantitative FinanceAlpha ModelingBacktesting
• CitadelMedium·Data Scientist
  Explain multicollinearity and OLS assumptions

  Explain multicollinearity and OLS assumptions 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 & MathTechnical Screen
• CitadelEasy·Data Scientist
  Find constant minimizing absolute error

  This question tests the understanding of L1 loss minimization and the statistical property that the median is the optimal point estimator under absolute error loss.

  Statistics & MathTechnical ScreenStatisticsOptimizationMedian
• CitadelMedium·Data Scientist
  Implement Infinite Fibonacci Generator Using Lazy Evaluation

  This question evaluates understanding of lazy evaluation and Python generator semantics, testing a candidate's ability to work with iterative sequence production and manage state across iterations.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement Piecewise Linear Interpolation and Extrapolation

  Implement piecewise linear interpolation from unsorted knots, including exact-knot behavior and endpoint extrapolation. Practice paired sorting, binary search, and numerical edge cases.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement lazy unique-merge generator for sorted streams

  This question evaluates knowledge of lazy evaluation, Python generators/iterators, merging nondecreasing streams with duplicate elimination, and considerations of time and space complexity in streaming contexts.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement two-pointer unique-pair sum search

  Implement two-pointer unique-pair sum search evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Match a string with wildcard pattern recursively

  This problem tests your ability to implement a recursive pattern-matching algorithm that handles specific wildcard characters while managing overlapping subproblems.

  Coding & AlgorithmsTechnical ScreenRecursionDynamic ProgrammingString Matching
• CitadelMedium·Data Scientist
  Stabilize LLM inference and estimate needed repeats

  This question tests the ability to design an LLM inference pipeline that ensures result stability through statistical aggregation and estimates the minimum number of inference samples required to achieve a target confidence level.

  ML System DesignTechnical ScreenLLMInferenceStatistics
• CitadelMedium·Data Scientist
  Update No-Intercept Regression Slopes Across Batches

  Compute cumulative no-intercept regression slopes as paired data arrives in batches. Reuse running cross-products and sums of squares instead of refitting all historical rows.

  Coding & AlgorithmsTechnical Screen
• CCloudTrucksMedium·Data Scientist
  Calculate Average Rate and Cumulative Jobs by State

  This question evaluates competency in relational data manipulation and analytical SQL concepts—including joins, aggregation with DISTINCT counts, per-date averaging, rolling cumulative counts, and handling numeric edge cases—within the Data Manipulation (SQL/Python) domain for a data scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• CoinbaseEasy·Data Scientist
  Calculate Expected Users Adopting Wallet Feature

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Users Adopting Wallet Feature states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• CoinbaseEasy·Data Scientist
  Calculate Expected Users Linking Coinbase Wallets

  Coinbase probability question covering Bernoulli indicators, expected wallet links, complement rule for at least one success, and conditional probability for two users linking wallets.

  Statistics & MathTechnical Screen
• CoinbaseHard·Data Scientist
  Diagnose a 20% retail revenue drop

  This question evaluates a data scientist's ability in revenue decomposition, diagnostic analytics, causal inference, and experimentation design, covering traffic×CVR×AOV breakdowns, post-purchase deductions, stockout impacts, rapid falsifiable checks, and difference-in-differences-style attribution.

  Analytics & ExperimentationTechnical Screen
• CoinbaseMedium·Data Scientist
  Diagnose a Sudden Revenue Decline: Analyses, Metrics, and Root-Cause Tests

  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 Diagnose a Sudden Revenue Decline: Analyses, Metrics, and Root-Cause Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• CoinbaseMedium·Data Scientist
  Estimate Super Bowl QR Code Scan Rate Using Historical Data

  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 Estimate Super Bowl QR Code Scan Rate Using Historical Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• CoinbaseMedium·Data Scientist
  Estimate Viewer Engagement with Super Bowl QR Code Promo

  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 Estimate Viewer Engagement with Super Bowl QR Code Promo states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• CoinbaseMedium·Data Scientist
  Investigate Anomalies in Coinbase Wallet Engagement Metrics

  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 Investigate Anomalies in Coinbase Wallet Engagement Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• CoinbaseEasy·Data Scientist
  Model Feature Adoption Probability for Users

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Model Feature Adoption Probability for Users states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• CoinbaseHard·Data Scientist
  Solve the 12-coin balance puzzle

  This question evaluates understanding of combinatorial reasoning, information theory, and decision-tree construction for identifying a single counterfeit coin and whether it is heavier or lighter using constrained balance-scale observations.

  Statistics & MathTechnical Screen
• 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
  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
• 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
• 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