TikTok Data Scientist candidates describe a four-round process beginning with recruiting and moving through technical interviews on SQL, experimentation, statistics, product analysis, ML basics, and project discussion.
$137K
Avg. Base Comp
$293K
Avg. Total Comp
4
Typical Rounds
Not reported
Process Length
TikTok Data Scientist interviews reported here begin with a recruiter screen followed by three technical rounds. The technical scope is broad: candidates describe multi-table SQL with joins and window functions, LeetCode-style SQL prompts, and one Python exercise used to solve a SQL-like problem. Practice explaining your query logic as you build it, because the reported interviews rewarded more than producing syntax alone.
Statistics and experimentation are another consistent thread. Candidates were asked about p-values, A/B-test design, and how to run an experiment from setup through interpretation. Product-oriented analysis also appeared, including a case about identifying the world’s most popular sport. Prepare to define an approach, clarify what success means, and connect the analysis to a decision rather than stopping at a metric.
A detailed resume or project discussion can be part of the technical evaluation. One candidate was asked to defend personal projects in depth, while another discussed a past project in a dedicated conversational round. ML coverage ranged from fundamentals with follow-up questions to a conceptual question about removing bias during backpropagation; one report also included a medium-style coding exercise. The reports are limited to two candidates, but together they point to preparation across SQL, experimentation, product reasoning, and clear project ownership.
Synthesized from 2 candidate reports by our editorial team.
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Featured question at Tiktok
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Merge Sorted Lists | |
| Raining in Seattle | |
| Retailer Data Warehouse | |
| WAU vs Open Rates | |
| Amateur Performance | |
| P-value to a Layman | |
| Compute Variance | |
| Google Maps Improvement | |
| Campaign Goals | |
| Basic Regex | |
| Marketing Channel Metrics | |
| Hurdles In Data Projects | |
| Post Success | |
| 7 Day Streak | |
| Flatten N-Dimensional Array to 1D Array | |
| Duplicate Rows | |
| Transformer Encoder Layer | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Target Value Search | |
| Unsafe Content ML Design | |
| Production Model Monitoring | |
| Bias vs. Variance Tradeoff | |
| Concurrent LLM Serving | |
| Data Preparation for Imbalanced Data | |
| TikTok Video Completions | |
| Overfit Avoidance | |
| Facebook Watch Party | |
| f(x,y) in Interval |
Synthesized from candidate reports. Individual experiences may vary.
Both candidates report a recruiter screen before technical interviews. Recruiters were described as professional or responsive; candidates should expect this stage to precede the reported technical sequence.
One candidate reported a detailed resume deep dive alongside SQL and ML basics. The SQL work used several tables, basic joins, and a window function, while ML questions could prompt follow-ups on the candidate’s explanation.
Candidates report questions on p-values, A/B testing, and end-to-end experiment design and interpretation. This stage may require connecting statistical reasoning to a product decision rather than reciting definitions.
Later interviews reportedly included a data-analysis or product-thinking case, LeetCode-style SQL, a Python solution to a SQL-like problem, and, in one report, medium-style coding. Project discussion may also be part of the conversation.