← Back to Directory

Thumbtack interview notes, page 3 of 3

Mid-sized

Most of these Thumbtack write-ups come from Data Scientists (19), then Product Managers (3). The most reported rounds are the technical phone screen (18), the product sense round (2), the cross-functional panel (1) and the system design round (1).

22 interview notes · updated Jul 2026

Thumbtack·Data Scientist·Technical Phone Screen

Jul 2026
Thumbtack data scientist interview that went deep on model evaluation and statistical fundamentals. The technical questions were genuinely hard and required connecting theory to practical constraints like temporal data and class imbalance.
  • Walk through the main cross-validation strategies (k-fold, stratified k-fold, leave-one-out, nested CV, time-series rolling and blocked CV) and explain when you'd use each one.
  • You have a dataset with temporal ordering and class imbalance. Design an evaluation framework that avoids data leakage while still giving stable performance estimates. Justify your fold construction and your choice of metrics.
  • Explain the bias-variance tradeoff quantitatively. How do model complexity, training set size, and regularization each affect bias and variance? What do CV error curves tell you about underfitting vs overfitting, and what levers do you pull depending on which problem you have?

“I actually felt okay on this until they pushed on nested CV.”

View Post

Thumbtack·Data Scientist·Technical Phone Screen

Jul 2026
Thumbtack data scientist interview, technical phone screen focused entirely on a single dense pandas problem involving response rate calculations, confidence intervals, and data quality handling. Pretty involved for a phone screen but I guess that's how they filter.
  • Given a CSV with job posting data (job_id, job_category, invitations_sent, provider_responses, region, created_at), write pandas code that: computes a per-job response rate while treating zero-invitation rows as missing; calculates an invitation-weighted response rate per job category with 95% Wilson score confidence intervals; returns the top 5 categories ranked by weighted rate with CI lower bound as tiebreaker; drops and logs rows with impossible values like negative counts or provider_responses exceeding invitations_sent; and verifies that the job-level weighted average matches the aggregate rate within floating point tolerance.

“This was a lot to hold in your head at once.”

View Post