TikTok data scientist candidates report a recruiter screen followed by technical rounds on SQL, statistics, A/B testing, machine learning and project deep dives, with some Python or algorithm questions.
$175K
Avg. Base Comp
$230K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Across five candidate reports, TikTok data scientist interviews usually begin with a recruiter or HR conversation and then move into technical discussions. SQL appears in most accounts. One candidate describes multi-table joins and a window function, another describes LeetCode-style SQL plus a Python version of a SQL problem, and a third describes window functions with CTEs. Another reports a medium-difficulty query using GROUP BY, filters and joins, followed by a Python or pandas version of the same logic.
The second pattern is experimentation and product reasoning. Candidates mention p-values, designing an A/B test end to end, and interpreting a metric that moves differently by channel. One candidate was asked about negative review rates for search purchases versus video or live purchases. The same candidate got a TikTok Shop business case on identifying fake listings, so practice asking clarifying questions and structuring an investigation.
Machine learning questions are also reported, including bias-variance tradeoff, tree-based models, and a backpropagation question about removing bias. Interviewers followed up beyond definitions. One candidate also had a hash-map algorithm question with the hiring manager, and another had a LeetCode-medium-style coding section.
Finally, several candidates describe detailed resume and project walkthroughs, so be ready to defend your methodology and decisions. Reported outcomes were mixed: one offer that the candidate declined and several rejections. Round counts varied, and no candidate gave an end-to-end timeline.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Tiktok process.
The first round focused on discussing my professional background, including my previous experience, technical skills, and projects. It also included technical questions on SQL and Python, where I was asked to demonstrate my understanding of database queries, data manipulation, and basic programming concepts.
Questions asked:
The SQL questions were of medium difficulty and focused on practical querying skills. One question involved writing a query to calculate aggregated metrics using GROUP BY and filtering results based on specific conditions. Another required using joins across multiple tables to retrieve the desired output. The Python question mirrored the SQL exercise, where I implemented the same data transformation and aggregation logic using Python data structures (or pandas) instead of SQL. The interviewer was primarily evaluating problem-solving ability, code organization, and whether I could explain my approach while coding.
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Topics based on recent interview experiences.
Featured question at Tiktok
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Monthly Customer Report | |
| Merge Sorted Lists | |
| Employee Salaries (ETL Error) | |
| Raining in Seattle | |
| Retailer Data Warehouse | |
| WAU vs Open Rates | |
| Amateur Performance | |
| P-value to a Layman | |
| Compute Variance | |
| Google Maps Improvement | |
| Basic Regex | |
| Campaign Goals | |
| Marketing Channel Metrics | |
| String Mapping | |
| Production Model Monitoring | |
| Hurdles In Data Projects | |
| Post Success | |
| 7 Day Streak | |
| Transformer Encoder Layer | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Flatten N-Dimensional Array to 1D Array | |
| Duplicate Rows | |
| Messenger Service Design | |
| Unsafe Content ML Design | |
| Target Value Search | |
| Concurrent LLM Serving | |
| Bias vs. Variance Tradeoff | |
| TikTok Video Completions |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter or HR conversation. Two describe the recruiter as responsive and organized. Treat it as a chance to confirm the team and the format of the technical rounds, since the later rounds appear to vary by team.
Candidates report SQL involving joins, aggregation, window functions or CTEs, sometimes with a Python or pandas version of the same logic. One account also included ML basics with follow-up questions, and another mentions a LeetCode-medium-style coding section.
Candidates describe rounds on p-values, A/B test design and interpretation, and open-ended product or business cases. Examples include a metric that differs by purchase channel and a TikTok Shop fake-listing scenario. Interviewers may value clarifying questions and a structured hypothesis.
Several candidates report detailed questions about past projects, including methodology and decisions. One account placed this in the first technical round, and another in a more behavioral round. Prepare to explain one project end to end.
One candidate reports a hiring manager round after the HR round, covering bias-variance tradeoff, tree-based models and a hash-map algorithm question. This is a single report, so the exact sequence may differ by team.
