TikTok interview questions

Showing 1–30 of 176 results

• Act when A/B result is not significantHard

  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.

  TikTokData ScientistStatistics & MathOnsite
• Analyze DFS, BFS, and A* trade-offsMedium

  This question evaluates understanding of graph search algorithms, heuristic properties (admissibility and consistency), path optimality and time/space complexity, requiring application of BFS, uniform-cost search, A*, and DFS on a concrete weighted graph.

  TikTokData ScientistCoding & AlgorithmsTechnical Screen
• Analyze Negative Reviews' Impact on Coupon Repurchase RateMedium

  Evaluates causal analysis of negative reviews on coupon repurchase behavior using exposure-aware data. Strong answers define treatment and outcome, collect exposure data, control confounding, and validate estimates with robustness checks.

  TikTokData ScientistAnalytics & ExperimentationTechnical Screen
• Analyze promo anomaly and design risk guardrailsMedium

  This question evaluates a candidate's ability to diagnose promotional anomalies, distinguish coordinated abuse from organic virality, design real-time approve/hold/reject decisioning with cost trade-offs, and specify experiment and monitoring plans, testing competencies in fraud analytics, operational risk modeling, and experimentation.

  TikTokData ScientistAnalytics & ExperimentationHR Screen
• Answer ML fundamentals and diagnostics questionsHard

  This problem tests your theoretical understanding of model diagnostics, ensemble learning mechanics, and the trade-offs between optimization strategies in machine learning.

  TikTokMachine Learning EngineerMachine LearningOnline Assessment
• Answer common behavioral questions using STARMedium

  This question assesses the candidate's ability to communicate past professional experiences using the STAR method, focusing on accountability, problem-solving, and collaboration in a high-pressure environment.

  TikTokSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer core Python, CS, DB, and testing questionsMedium

  This problem tests foundational software engineering knowledge across Python internals, database indexing, and testing strategy through a multi-part conceptual assessment.

  TikTokSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Balance Customer Satisfaction with Fraud Prevention: Key Metrics to TrackMedium

  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.

  TikTokData ScientistAnalytics & ExperimentationOnsite
• Break a Palindrome AlgorithmMedium

  Practice the greedy algorithm for breaking a palindrome into the lexicographically smallest non-palindrome by one character replacement. The solution explains scanning the left half, skipping the middle character, changing the first non-a to a, handling all-a cases, proving correctness, and analyzing complexity.

  TikTokProduct ManagerProduct / Decision MakingOnline Assessment
• Calculate Conversion Rate from Ad Clicks to Page VisitsMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Day-7 Retention Rate from User Post DataMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Expected Draws for X > 0.8 in Uniform(0,1)Easy

  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.

  TikTokData ScientistStatistics & MathTechnical Screen
• Calculate User Registration Date and 7-Day Retention RateMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate valid daily usage with gap constraintsMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Compare Random Forests and Boosted Trees: Bias, Variance, SpeedMedium

  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.

  TikTokData ScientistMachine LearningTechnical Screen
• Compute 7-day rolling complaint/order ratio in SQLMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute Averages of Unique Numbers in Dictionary ListsMedium

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

  TikTokData ScientistCoding & AlgorithmsOnsite
• Compute CTR drop with exclusionsMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Onsite
• Compute and rank top bad advertisersMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute city skyline silhouetteMedium

  Compute city skyline silhouette 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.

  TikTokMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Compute cluster-aware significance and sequential correctionsMedium

  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.

  TikTokData ScientistStatistics & MathHR Screen
• Compute last-to-previous ad impression gapsMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute length of longest increasing subsequenceMedium

  This question evaluates understanding of subsequences, dynamic programming and algorithmic optimization for array processing, and is categorized under Coding & Algorithms; it emphasizes practical application of algorithm design and complexity analysis rather than purely theoretical knowledge.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute minimum path sum in a triangleMedium

  This problem tests your ability to optimize path calculations in a non-linear data structure using dynamic programming, specifically focusing on space complexity optimization.

  TikTokMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Compute pipeline order from dependenciesMedium

  This question evaluates understanding of graph algorithms and dependency resolution, specifically topological sorting and cycle detection, along with competence in choosing appropriate data structures and analyzing time/space complexity.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Control confounding in observational ad liftHard

  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.

  TikTokData ScientistStatistics & MathOnsite
• Count buggy vs non-buggy by employerMedium

  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.

  TikTokData ScientistData Manipulation (SQL/Python)Online Assessment
• Count deletions making array fairMedium

  This question evaluates proficiency with array manipulation, parity-aware summation, and combinatorial counting when elements are removed, situating it in the Coding & Algorithms domain.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count distinct island shapes in a gridMedium

  This problem tests grid traversal (DFS/BFS) and the ability to represent spatial structures in a canonical form to identify unique patterns.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count islands on a torus gridMedium

  This question evaluates skills in graph and grid algorithms, topology-aware adjacency (torus wrap-around), and maintaining dynamic connectivity under incremental updates.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen