Data Scientist Interview Questions

Showing 1741–1770 of 1971 results

• TikTokEasy·Data Scientist
  Write SQL for TikTok Live creator metrics

  This question evaluates SQL data-manipulation competencies such as joins, aggregations, group-by and window functions, handling missing activity rows, date-range filtering, country partitioning, and deterministic tie-breaking for top-k metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Write SQL for geo posting-frequency drops

  This question evaluates proficiency in SQL-based time-series data manipulation, including time zone–aware date normalization, window functions, aggregation, filtering and median computations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• TikTokMedium·Data Scientist
  Write SQL for last-7-day metrics without windows

  This question evaluates proficiency in ANSI SQL data manipulation, including aggregation, de-duplication, date arithmetic, joins and subqueries, GROUP BY/HAVING usage, and cohort/retention and conversion calculations performed without window functions.

  Data Manipulation (SQL/Python)Technical Screen
• TFTools For HumanityEasy·Data Scientist
  Compute reply-based engagement metrics

  This question evaluates proficiency in data manipulation and analytics, emphasizing SQL/Python skills for temporal filtering, joins between posts and users, deduplication, aggregation (distinct counts), and percentage calculations used to compute reply-based engagement metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TFTools For HumanityEasy·Data Scientist
  Detect and evaluate "stolen" posts

  This question evaluates data-science competencies in instrumentation and feature design, content-similarity detection and labeling, causal inference for experiments, measurement of harms, and model evaluation, and belongs to the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• TTowerHard·Data Scientist
  Debug Oscillating GPU Utilization During Training

  Design a systematic debugging and architectural remediation strategy for a distributed deep learning training cluster exhibiting severe GPU utilization oscillation between 90% and 20%.

  ML System DesignTechnical Screendistributed-trainingml-system-designgpu-optimization
• TTowerHard·Data Scientist
  Explain Why Constant Neural-Network Initialization Fails

  This question tests the conceptual understanding of weight initialization strategies and the symmetry-breaking property required for neural network gradient-based optimization.

  Machine LearningTechnical ScreenDeep LearningInitializationOptimization
• TTransunionMedium·Data Scientist
  Describe past project experience clearly

  This prompt evaluates a candidate's competencies in applied data science tools and practices—including proficiency in Python or R, SQL querying and data transformation, problem scoping under ambiguity, technical-to-nontechnical communication, and team coordination.

  Behavioral & LeadershipTechnical Screen
• 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
• TuroHard·Data Scientist
  Compare XGBoost and LightGBM

  This question evaluates a data scientist's understanding of gradient-boosted tree implementations and practical model trade-offs, covering competencies in algorithmic differences, performance and memory characteristics, handling of missing and categorical features, regularization risks, and hyperparameter considerations for production models.

  Machine LearningOnsite
• TuroHard·Data Scientist
  Identify Booking Drivers

  This question evaluates skills in product analytics, causal inference, feature importance modeling, metric definition, and experimental validation within a marketplace context.

  Analytics & ExperimentationOnsite
• 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 SigmaMedium·Data Scientist
  Analyze NYC taxi trips efficiently over last 7 days

  This question evaluates proficiency in time-series filtering, relational joins, grouped aggregations (median and high-percentile calculations), numeric stability of percentile methods, and performance-aware vectorized data manipulation using SQL or pandas.

  Data Manipulation (SQL/Python)Online Assessment
• Two SigmaMedium·Data Scientist
  Analyze Temperatures and Update Regression

  This question evaluates proficiency in time-series and statistical analysis (defining volatility and similarity metrics), supervised regression modeling with mean squared error evaluation, greedy feature selection, and streaming/no-intercept linear regression updates.

  Machine LearningOnline Assessment
• 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
  Build a Housing Price Prediction Model from Historical Data

  This question evaluates a data scientist's competency in end-to-end supervised machine learning for real-estate pricing, focusing on precise outcome definition, feature engineering, handling missing and time-varying data, and robustness to market regime changes.

  Machine LearningOnsite
• Two SigmaMedium·Data Scientist
  Compute Piecewise Linear Interpolation

  This question evaluates skills in algorithm design and numerical interpolation, including sorting and searching among 2D points, handling boundary cases for extrapolation, and computing linear interpolation accurately and efficiently.

  Coding & AlgorithmsOnline Assessment
• Two SigmaHard·Data Scientist
  Count Distinct Island Shapes in a Binary Grid

  This question evaluates a candidate's understanding of grid-based connected components and spatial equivalence, measuring competency in representing and distinguishing island shapes regardless of their location.

  Coding & AlgorithmsOnsite
• 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