What to expect in the Thumbtack data scientist interview, based on three candidate reports: a recruiter call, a technical screen on marketplace SQL and cohort questions, a takehome, and open-ended experiment design.
$161K
Avg. Base Comp
$185K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Thumbtack data scientist candidates describe an interview that starts with a recruiter call and moves into a technical screen. The questions they recall center on the two-sided marketplace business. One candidate was asked three marketplace-focused questions, including calculating monthly cohort counts for new versus returning customers. They found the questions foundational, more a test of business-model understanding than of advanced technique. Another candidate recalls a technical screen covering SQL and a case study. That candidate passed it.
A third candidate, interviewing for a senior role, got an open-ended incrementality testing question. They answered with a B2B framing, using account-level holdouts and longer conversion windows. The interviewer wanted a B2C example and ended the session. If you get an experiment design prompt, it is reasonable to confirm the business context before you commit to a framework.
The first candidate learned that the team's focus was pricing and optimization around it. They later felt this was a mismatch with their background. Practice cohort and customer segmentation SQL, and be ready to discuss pricing and two-sided marketplace dynamics. That candidate was told to expect a technical interview, a panel and a meeting with the VP of Data Science, and went through a takehome before being rejected.
All three reports ended without an offer. Two describe slow or unclear communication, so ask about next steps and the timeline at each stage. With only three reports, treat this as a picture of what some candidates saw, not a fixed format.
Synthesized from 3 candidate reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Thumbtack
Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Digital Marketing Metrics | |
| Text Editor With OOP | |
| Account Personalization Strategy | |
| Your Strengths and Weaknesses | |
| Evaluating Revenue Decline | |
| Food Delivery Refund Policy | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Experiment Validity | |
| Merge Sorted Lists | |
| Top Three Salaries | |
| Customer Orders | |
| Monthly Customer Report | |
| Closest SAT Scores | |
| Button AB Test | |
| Employee Salaries | |
| Download Facts | |
| Subscription Overlap | |
| First to Six | |
| 500 Cards | |
| Upsell Transactions | |
| First Touch Attribution | |
| User Experience Percentage | |
| Month Over Month | |
| Weighted Keys | |
| Network Experiment Design | |
| Compute Deviation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter call. One found it friendly and said the recruiter outlined later rounds: a technical interview, a panel and a VP of Data Science meeting. Another reported the recruiter running late and unclear scheduling, so confirm timing and next steps in writing.
Candidates report a technical screen. One recalled three two-sided marketplace questions, including monthly cohort counts for new versus returning customers, and called them foundational. Another described SQL plus a case study. Prepare cohort SQL and business reasoning about two-sided marketplaces.
One senior candidate was asked an open-ended incrementality testing question. They answered from a B2B perspective, but the interviewer wanted a B2C example and ended the interview. It may help to clarify the business model and assumptions before laying out a lift measurement approach.
One candidate completed a takehome after the technical interview and was then rejected without feedback. That same candidate had been told a panel and a VP of Data Science meeting could follow, but did not report reaching them. They also learned the team focused on pricing and optimization.
