Data Scientist Interview Questions

Showing 571–600 of 641 results

• ThumbtackHard·Data Scientist
  Design a robust pro-ranking A/B test

  It evaluates experimental design and causal inference competencies for two-sided marketplaces, focusing on metric definition and guardrails, randomization to limit interference, power and sample-size estimation, bias controls, monitoring and sequential stopping, and attribution of uplift versus cannibalization.

  Analytics & ExperimentationOnsite
• ThumbtackMedium·Data Scientist
  Implement min, mean, median robustly

  This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.

  Coding & AlgorithmsOnsite
• ThumbtackHard·Data Scientist
  Lead XFN decision under tight timeline

  This question evaluates cross-functional leadership, stakeholder alignment, rapid decision-making under time pressure, data triage and technical judgment, executive communication, and risk management skills.

  Behavioral & LeadershipOnsite
• ThumbtackMedium·Data Scientist
  Test regional response-rate differences rigorously

  This question evaluates applied statistical modeling and causal-inference competencies, including adjustment for confounding, analysis of proportion/count outcomes, variance estimation under clustering, multiple-comparison control, diagnostic detection of Simpson’s paradox, and clear effect-size reporting; it is categorized under Statistics & Math and emphasizes practical application with necessary conceptual understanding. Such problems are commonly asked to assess the ability to justify and interpret comparative analyses that account for covariate mix, temporal structure, and multiple-testing concerns, and this summary is in English.

  Statistics & MathOnsite
• TikTokHard·Data Scientist
  Act when A/B result is not significant

  This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.

  Statistics & MathOnsite
• TikTokMedium·Data Scientist
  Balance Customer Satisfaction with Fraud Prevention: Key Metrics to Track

  Evaluates risk-decisioning trade-offs between customer experience and fraud prevention in payment products. Strong answers cover cost-sensitive thresholds, false declines, fraud loss, experimentation, monitoring, and delayed labels.

  Analytics & ExperimentationOnsite
• TikTokMedium·Data Scientist
  Compute Averages of Unique Numbers in Dictionary Lists

  This question evaluates data manipulation and aggregation skills, focusing on handling duplicate values within collections and computing numerical summaries of list data.

  Coding & AlgorithmsOnsite
• TikTokMedium·Data Scientist
  Compute CTR drop with exclusions

  This question evaluates a candidate's ability to compute time-windowed click-through rates using SQL aggregations, joins, filters and data-cleaning (excluding suspicious ads) while handling edge cases such as missing days, minimum-impression thresholds, division-by-zero, and integer-division pitfalls.

  Data Manipulation (SQL/Python)Onsite
• TikTokHard·Data Scientist
  Control confounding in observational ad lift

  This question evaluates mastery of observational causal inference for ATE estimation, covering causal DAGs and identification, pre-treatment adjustment, propensity-score and inverse-probability/doubly-robust estimation, diagnostic checks, sensitivity analysis for unobserved confounding, and variance/uncertainty quantification in the context of ad exposure data. It is commonly asked because real-world advertising analyses cannot rely on randomization and interviewers need assurance of both conceptual understanding of identification assumptions and practical application of estimation and diagnostic techniques; the category is Statistics & Math and the level of abstraction spans conceptual reasoning and applied implementation.

  Statistics & MathOnsite
• TikTokHard·Data Scientist
  Design A/B Test for Cost-Per-Conversion Efficiency Analysis

  Design A/B Test for Cost-Per-Conversion Efficiency Analysis evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• TikTokHard·Data Scientist
  Design Real-Time Credit Card Fraud Detection System

  Evaluates real-time credit-card fraud detection system design under latency, drift, and asymmetric costs. Strong answers cover data, delayed labels, features, models, serving architecture, monitoring, retraining, and thresholding.

  Machine LearningOnsite
• TikTokMedium·Data Scientist
  Design a creator posting-frequency experiment

  This problem tests the ability to design a controlled experiment while accounting for network interference, non-compliance, and selection bias in a two-sided platform.

  Analytics & ExperimentationOnsiteExperimentationA/B TestingCausal Inference
• TikTokEasy·Data Scientist
  Design multimodal deployment under compute limits

  This question evaluates competency in Machine Learning systems engineering for a Data Scientist role, covering multimodal model deployment and inference optimization, scalable retrieval (ANN and hybrid search), generalization and regularization concepts (overfitting, dropout), normalization methods, and RLHF, emphasizing both theoretical principles and engineering feasibility. It is commonly asked to assess reasoning about trade-offs between quality, latency, and cost in resource-constrained environments, validation via offline/online metrics, and the ability to bridge conceptual understanding with practical deployment and system-level design considerations.

  Machine LearningOnsite
• TikTokMedium·Data Scientist
  Diagnose Search Issues with Relevant Metrics and Solutions

  Evaluates diagnosis of brand-name search relevance and safety issues using analytics and experimentation. Strong answers define relevance, satisfaction, and harmful-content metrics, find root causes, and validate fixes offline and online.

  Analytics & ExperimentationOnsite
• TikTokHard·Data Scientist
  Diagnose metric drop in Ads Manager

  This question evaluates a Data Scientist's competency in operational analytics, metric integrity, root-cause analysis, and experiment interpretation, covering concepts such as logging latency, sample ratio assessment, and product instrumentation awareness.

  Analytics & ExperimentationOnsite
• TikTokMedium·Data Scientist
  Improve TikTok's Algorithm for Diverse Content Discovery

  Evaluates product feedback and experiment design for improving content discovery in TikTok-style feeds. Strong answers propose specific improvements, justify impact, define metrics, and plan experiments with guardrails.

  Analytics & ExperimentationOnsite
• TikTokHard·Data Scientist
  Measure Billboard Campaign Effectiveness and Engagement Quantification

  Evaluates offline marketing measurement for a billboard campaign, including how to define attributable engagement, estimate exposed population, and forecast incremental traffic, sign-ups, retention, revenue, and ROI. Strong answers use geo-lift, matched markets, or synthetic controls rather than direct attribution alone.

  Analytics & ExperimentationOnsite
• TikTokMedium·Data Scientist
  Optimize Station Sequence for Maximum Car Output in Simulation

  This question evaluates a candidate's skills in scheduling and throughput optimization, including queue management and simulation-based performance analysis within constrained buffering and station sequencing.

  Coding & AlgorithmsOnsite
• TikTokMedium·Data Scientist
  Resolve Stakeholder Conflicts: Actions and Outcomes Explained

  Evaluates behavioral stakeholder-conflict storytelling for data scientists delivering products or experiments. Strong answers use STAR, show empathy, structure trade-offs, drive decisions, and quantify outcomes.

  Behavioral & LeadershipOnsite
• TikTokHard·Data Scientist
  Test 15s to 60s video length change

  This question evaluates a data scientist's competency in experimental design, causal inference, metric definition and instrumentation, power/MDE calculations, interference and contamination handling on multi‑sided platforms, and rollout planning.

  Analytics & ExperimentationOnsite
• TikTokEasy·Data Scientist
  Test Billboard Campaign Conversion Rate Exceeds 60%

  Evaluates one-sample proportion testing for a billboard conversion rate with 65 conversions out of 100 users. Strong answers state one-sided hypotheses, compute the standard error and confidence interval, run the correct z test against 60%, and interpret why statistical evidence may be insufficient.

  Statistics & MathOnsite
• 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
• 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
• 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 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
  Implement Univariate Linear Regression with Ordinary Least Squares

  This question evaluates understanding of univariate linear regression and the ordinary least squares (OLS) closed-form estimator, probing competencies in basic statistical estimation, interpretation of slope and intercept, numerical stability, and floating-point arithmetic.

  Coding & AlgorithmsOnsite
• Two SigmaHard·Data Scientist
  Maximum-Product Simple Path in a Complete Directed Graph

  This question evaluates skills in graph algorithms, multiplicative path optimization, and combinatorial dynamic programming, testing competency in reasoning about simple paths and product-based scoring in directed weighted graphs.

  Coding & AlgorithmsOnsite
• UberMedium·Data Scientist
  Describe Conflict and Impact

  This question set evaluates leadership, communication, stakeholder management, decision-making under ambiguity, and technical project ownership for a data scientist, including the ability to quantify impact and reflect on failures.

  Behavioral & LeadershipOnsite
• UberMedium·Data Scientist
  Describe ownership and failure

  This question evaluates ownership, judgment, self-awareness, and the ability to communicate technical depth to non-experts and senior stakeholders, while also probing data-science-specific competencies such as forecasting, experimentation, causal inference, and measuring impact.

  Behavioral & LeadershipOnsite