Data Scientist Interview Questions

Showing 1681–1710 of 1971 results

• 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
  Calculate Conversion Rate from Ad Clicks to Page Visits

  This question evaluates a candidate's competency in event-level data manipulation and metric computation using SQL or Python, including time-based aggregation, deduplication, and joining or windowing of event streams.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate Day-7 Retention Rate from User Post Data

  This question evaluates a candidate's competency in SQL and Python data wrangling, emphasizing date arithmetic, window functions, grouping, and conditional aggregation for cohort and retention calculations.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokEasy·Data Scientist
  Calculate Expected Draws for X > 0.8 in Uniform(0,1)

  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 Draws for X > 0.8 in Uniform(0,1) states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• TikTokMedium·Data Scientist
  Calculate User Registration Date and 7-Day Retention Rate

  This question evaluates the ability to perform time-based data manipulation and cohort retention analysis using SQL/Python, including deriving registration dates, aggregating posts within time windows, and computing retention metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate valid daily usage with gap constraints

  This question evaluates a candidate's ability to manipulate temporal intervals, including clipping to a specified day window, merging touching or overlapping segments, detecting gaps, and aggregating minute-level durations using SQL window functions and date/time arithmetic, and belongs to the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compare Random Forests and Boosted Trees: Bias, Variance, Speed

  Evaluates practical trade-offs between Random Forests and gradient-boosted trees for tabular ML. Strong answers compare bias, variance, speed, interpretability, overfitting, production fit, and feature scaling needs.

  Machine LearningTechnical Screen
• TikTokMedium·Data Scientist
  Compute 7-day rolling complaint/order ratio in SQL

  This question evaluates SQL data manipulation skills, specifically time-based aggregations, sliding window functions, joins to include entities with no events, and numeric casting within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• TikTokMedium·Data Scientist
  Compute and rank top bad advertisers

  This question evaluates proficiency in SQL data manipulation and analytics, including time-window filtering, JOINs, aggregation and deduplication, rate calculations, and window-function–based ranking.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compute cluster-aware significance and sequential corrections

  This question evaluates competency in clustered randomized experiment analysis, including calculation of design effect and effective sample size, cluster-robust inference for differences in proportions, sequential alpha spending (O’Brien–Fleming-style) and comparisons with Bonferroni, Holm–Bonferroni adjustments for multiple guardrail metrics, and Bayesian ROPE interpretation. It is in the Statistics & Math domain and is commonly asked to probe how candidates handle intra-cluster correlation, control Type I error across interim looks and multiple metrics, and demonstrate both conceptual understanding and practical application of power, duration, and multiplicity trade-offs.

  Statistics & MathHR Screen
• TikTokMedium·Data Scientist
  Compute last-to-previous ad impression gaps

  This question evaluates proficiency in data manipulation and time-based analytics, covering SQL window functions, timestamp arithmetic, and grouping/ordering semantics within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• TikTokMedium·Data Scientist
  Count buggy vs non-buggy by employer

  This question evaluates proficiency in SQL data manipulation—particularly conditional aggregation and join semantics—for counting categorized records across related tables in a data science context.

  Data Manipulation (SQL/Python)Online Assessment
• TikTokMedium·Data Scientist
  Demonstrate leadership in cross-functional disagreement

  This question evaluates leadership, cross-functional collaboration, stakeholder management, experimental design, and metric-driven decision-making competencies in a data scientist context, with emphasis on trade-off reasoning and end-to-end execution.

  Behavioral & LeadershipHR Screen
• TikTokMedium·Data Scientist
  Derive L1 vs L2 effects with correlation

  This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.

  Statistics & MathTechnical Screen
• TikTokMedium·Data Scientist
  Describe Your Most Impactful Project Experience and Lessons Learned

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Impactful Project Experience and Lessons Learned states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• 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
• TikTokMedium·Data Scientist
  Design Metrics for Content Moderation and Chatbot Evaluation

  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 Design Metrics for Content Moderation and Chatbot Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• 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
• TikTokHard·Data Scientist
  Design an ad-selection system across objectives

  This question evaluates expertise in machine learning system design for ad selection and real-time bidding, covering predictive modeling and calibration, value unification across objectives, auction mechanics, budget pacing, exploration strategies, guardrails, and evaluation methodologies.

  Machine LearningTechnical Screen
• TikTokHard·Data Scientist
  Design an experiment for exploratory recommendations

  Evaluates experiment design for exploratory recommendation algorithms that may trade short-term engagement for discovery. Strong answers define exploration, satisfaction, retention, ecosystem health, leading indicators, and cautious launch decisions.

  Analytics & ExperimentationTechnical Screen
• TikTokHard·Data Scientist
  Design an interference-robust A/B test for monetization

  This question evaluates experimental design and causal inference skills within Analytics & Experimentation, emphasizing interference mitigation, clustering and randomization choices, eligibility and exposure rules, metric hierarchy and guardrails, power and duration planning, variance-reduction techniques, and cluster-robust inference in a two-sided marketplace. It is commonly asked to test a data scientist's practical-application ability to balance monetization and growth trade-offs through precise statistical planning, pre-registered stopping rules, SRM detection, MDE and rollout decision thresholds rather than purely conceptual understanding.

  Analytics & ExperimentationHR Screen
• 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
• TikTokHard·Data Scientist
  Design recommendations objective balancing growth and monetization

  This question evaluates a data scientist's competency in designing multi-objective recommender systems that balance user growth and creator monetization, covering ranking objective formulation, per-impression value estimation, constraint enforcement (latency, diversity, fairness/exposure), offline-to-online metric validation, cold-start and exploration strategies, and interference/attribution challenges within the Machine Learning domain. It is commonly asked to assess the ability to reason about trade-offs between business and user metrics, to define measurable objectives and validation plans, and to demonstrate both conceptual understanding and practical application of system design and evaluation.

  Machine LearningHR Screen
• TikTokHard·Data Scientist
  Design robust A/B test with interference and seasonality

  This question evaluates expertise in experimental design, causal inference, statistical power and minimum detectable effect calculation, variance-reduction techniques, sequential monitoring, and diagnostics for interference, spillovers, and weekly seasonality in A/B testing.

  Analytics & ExperimentationTechnical Screen
• 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
• TikTokMedium·Data Scientist
  Diagnose Traffic Allocation in A/B Test Results

  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 Traffic Allocation in A/B Test Results states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen