Data Scientist Interview Questions

Showing 931–960 of 1076 results

• TTransunionEasy·Data Scientist
  How would you answer a DS video interview?

  TransUnion's Data Scientist Intern technical screen is an asynchronous video interview with five ~2-minute prompts covering Python/R problem solving, SQL usage, working under ambiguity, explaining technical work to non-technical stakeholders, and keeping a team aligned. This guide gives a per-prompt structure, the evaluator rubric, strong sample outlines, and the common mistakes to avoid.

  Behavioral & LeadershipTechnical Screen
• TubiMedium·Data Scientist
  Compute daily net users from event logs

  This question evaluates SQL-based data manipulation skills focused on daily aggregation, cohort and retention calculations, deduplicating event records, and grouping metrics by device_platform.

  Data Manipulation (SQL/Python)Technical Screen
• TubiHard·Data Scientist
  Determine A/B test sample size drivers

  This question evaluates statistical power and sample size determination for A/B testing, covering hypothesis testing for proportions, derivation using normal approximations, the impact of MDE, α and power choices, allocation ratios, cluster design (ICC and design effect), group‑sequential monitoring, and adjustment for overdispersed count outcomes.

  Analytics & ExperimentationTechnical Screen
• TubiHard·Data Scientist
  Handle multiple experiment metrics and communication

  This question assesses the ability to design a multi-metric experiment, manage the false positive rate under multiple testing, and communicate trade-off decisions to non-technical stakeholders.

  Analytics & ExperimentationTechnical ScreenA/B TestingStatistical SignificanceMultiple Comparisons
• TTudorMedium·Data Scientist
  Estimate Volatility from Gaussian and Brownian Samples

  This question evaluates skills in statistical estimation and stochastic process modeling, specifically parameter estimation for Gaussian samples and volatility estimation from discrete Brownian motion observations, focusing on estimating the volatility parameter (sigma) or its variance (sigma^2).

  Machine LearningTechnical Screen
• TwitchMedium·Data Scientist
  Analyze Streaming Data with SQL for Monthly Trends

  This question evaluates proficiency in SQL time-series aggregation, date extraction and grouping across calendar months, join and conditional aggregation techniques, windowed month-over-month comparisons, and handling sparse or multi-year datasets; Category: Data Manipulation (SQL/Python); level: practical application.

  Data Manipulation (SQL/Python)Technical Screen
• TwitchHard·Data Scientist
  Design real-time live-stream recommendations

  This question evaluates competency in designing real-time recommender systems within the Machine Learning domain, covering objectives and label construction for streaming events, feature engineering across user/creator/stream/context, model and loss choices, bias mitigation, evaluation metrics, and low-latency serving and freshness constraints.

  Machine LearningTechnical Screen
• TwitchMedium·Data Scientist
  Explain motivation and behavioral principles

  This question evaluates a data scientist's motivational fit and behavioral leadership competencies, including communication under time pressure, stakeholder management, handling ambiguous requirements, prioritization, experimental design reasoning, and influencing without formal authority.

  Behavioral & LeadershipTechnical Screen
• TwitchMedium·Data Scientist
  Solve advanced SQL for streaming analytics

  This question evaluates advanced SQL competency for streaming analytics — covering time-series aggregation, monthly bucketing across year boundaries, joins between event tables, window functions, and case-insensitive substring filtering; the domain is Data Manipulation (SQL/Python) and the level is practical application of SQL techniques.

  Data Manipulation (SQL/Python)Technical Screen
• Two SigmaHard·Data Scientist
  Add two integer strings using digit-adder

  This problem tests string manipulation, modular arithmetic, and the ability to manage carry-over logic manually to perform addition on integers that exceed the capacity of standard numeric data types.

  Coding & AlgorithmsTechnical ScreenString ManipulationTwo PointersArithmetic
• Two SigmaEasy·Data Scientist
  Answer four core statistics questions

  This multipart problem evaluates competency in probability and mathematical statistics, covering order statistics and covariance, the rationale for degrees of freedom in sample variance, z-test sample size calculation, and a one-sided concentration inequality, categorized under Statistics & Math for a Data Scientist role and combining conceptual derivations with practical formula application. Such questions are commonly asked to verify mathematical rigor in deriving expectations and covariances, understanding sampling distributions and unbiased estimation, applying hypothesis testing power formulas, and proving probabilistic inequalities that underpin statistical reasoning.

  Statistics & MathTechnical Screen
• Two SigmaHard·Data Scientist
  Assign 2n Workers to Two Tasks to Minimize Total Time

  This question evaluates algorithmic problem-solving skills in combinatorial optimization and resource allocation, focusing on reasoning about cost differences when assigning 2n workers into two equal-sized tasks and belongs to the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen
• Two SigmaHard·Data Scientist
  Derive correlation bounds and omitted-variable bias

  This problem tests your understanding of the constraints on correlation matrices and your ability to analytically derive the bias introduced in linear models when a relevant predictor is ignored.

  Machine LearningTechnical ScreenLinear RegressionLinear AlgebraStatistics
• Two SigmaEasy·Data Scientist
  Design a house-price prediction model

  This problem evaluates your ability to design a robust end-to-end machine learning pipeline for house price prediction, focusing on data preprocessing, feature engineering, and validation strategy.

  Machine LearningTechnical ScreenRegressionFeature EngineeringModel Validation
• Two SigmaMedium·Data Scientist
  Design features for house price prediction

  This question tests your ability to engineer features from heterogeneous tabular data, handle missing values, and select appropriate evaluation metrics for a high-stakes regression problem.

  Machine LearningTechnical ScreenFeature EngineeringRegressionData Preprocessing
• Two SigmaHard·Data Scientist
  Expected Number of Good Pairs in a Randomly Weighted Complete Graph

  This question evaluates probabilistic reasoning, combinatorics, and order-statistics intuition by asking for the expected count of mutual maximum-edge neighbors (good pairs) in an edge-weighted complete graph with i.i.d. continuous weights.

  Coding & AlgorithmsTechnical Screen
• Two SigmaHard·Data Scientist
  Explain why the t-statistic helps

  This question evaluates understanding of the t-statistic, standardized effect sizes, standard errors, hypothesis testing, and interpretation of statistical evidence within the Statistics & Math domain, and it targets conceptual understanding with practical application implications.

  Statistics & MathTechnical Screen
• Two SigmaHard·Data Scientist
  Forecast bikes available at a station

  This question tests the ability to design a robust time-series forecasting pipeline, specifically addressing feature engineering for cyclic temporal patterns and the selection of appropriate validation strategies for sequential data.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringForecasting
• Two SigmaMedium·Data Scientist
  How detect duplicate card records?

  This question evaluates a candidate's competency in duplicate detection, record linkage, entity resolution, and data quality assessment within credit card transaction datasets.

  Machine LearningTechnical Screen
• Two SigmaEasy·Data Scientist
  How to forecast bike dock demand

  This problem tests your ability to design a predictive pipeline for spatio-temporal demand, focusing on feature extraction from time-series data and robust validation strategies.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringValidation
• Two SigmaHard·Data Scientist
  How would you forecast bike demand?

  This problem tests your ability to design a robust machine learning pipeline for time-series demand forecasting, focusing on feature engineering for exogenous variables and evaluation metrics for spatial-temporal data.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringRegression
• Two SigmaMedium·Data Scientist
  Implement merge sort and largest 1-rectangle

  This question evaluates proficiency in implementing and analyzing classic algorithms, specifically merge sort for array sorting and techniques for computing the largest all-ones rectangular submatrix in a binary matrix, testing algorithmic thinking, data structure manipulation, and time/space complexity reasoning.

  Coding & AlgorithmsTechnical Screen
• Two SigmaEasy·Data Scientist
  Match readings with latest same-city humidity

  This question evaluates the ability to align and join time-ordered sensor data by key, testing skills in time-series merging, per-key state management, and handling absent matches.

  Coding & AlgorithmsTechnical Screen
• Two SigmaHard·Data Scientist
  Merge Two Sorted Lists

  This problem tests your ability to manipulate linked list pointers to merge two sorted sequences efficiently without allocating extra space for new nodes.

  Coding & AlgorithmsTechnical ScreenLinked ListsPointersRecursion
• Two SigmaHard·Data Scientist
  Merge two sorted linked lists

  This problem tests your ability to manipulate linked list pointers to merge two sorted structures in-place, focusing on space optimization and handling edge cases.

  Coding & AlgorithmsTechnical ScreenLinked ListsPointersIn-place
• Two SigmaHard·Data Scientist
  Predict Citi Bike Demand at a Specific NYC Station

  This question evaluates time-series forecasting, demand estimation, and ML system-design competencies, focusing on problem framing, handling censored observations, data integration, model selection, and evaluation under operational constraints.

  ML System DesignTechnical Screen
• Two SigmaMedium·Data Scientist
  Predict Stock Prices from Google Search Data

  This question evaluates a candidate's competency in time-series and cross-sectional predictive modeling, alternative-data feature engineering, and rigorous validation to prevent temporal leakage within the Machine Learning domain.

  Machine LearningTechnical Screen
• Two SigmaHard·Data Scientist
  Predict a Company's Stock Price from Six Years of Web Search Logs

  This question evaluates proficiency in feature engineering and time-series forecasting from large-scale event logs, including entity linking, aggregation and normalization, handling non-stationarity and drift, and model selection and validation for predicting a given company's stock price.

  Machine LearningTechnical Screen
• Two SigmaHard·Data Scientist
  Predict bike demand and avoid overfitting

  This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.

  Machine LearningTechnical Screen
• Two SigmaHard·Data Scientist
  Predicting Stock Prices from Twitter Data

  This question evaluates competency in ML system design for integrating NLP-derived signals from social media with time-series financial modeling, including feature extraction, timestamp alignment, and handling low signal-to-noise ratios.

  ML System DesignTechnical Screen