Pinterest Data Scientist candidates report recruiter screening, technical evaluation, and behavioral or cross-functional interviews. Reported technical work includes SQL, Python, statistics, and experiment-design reasoning.
$310K
Avg. Base Comp
$469K
Avg. Total Comp
3-8 rounds
Typical Rounds
5 weeks
Process Length
Pinterest Data Scientist interviews in these reports combine practical technical work with communication and analytical judgment. SQL, Python, statistics, and experiment design are the clearest recurring preparation areas. One candidate described a 60-minute technical screen spanning SQL, Python, and statistics or experimentation, while another described an experiment-design discussion focused on metrics, sample size, long-term engagement, and confounders.
For SQL, practice explaining your reasoning as you build a query. Reported questions included pin popularity and a window-functions problem. For Python, be comfortable turning a product-data prompt into clear code; one candidate recalled a pin-similarity exercise using a dictionary.
Experimentation preparation should cover the full path from objective and metric selection through interpretation. Candidates reported follow-up on whether short-term results can mislead and on how to account for confounders. A billboard-exposure experiment-design prompt was also reported.
The process may include behavioral or cross-functional discussion. Prepare concise examples that show how you communicate analytical tradeoffs and partner with stakeholders. One recent account reported an approximately five-week, fully virtual process with a two-day onsite, while other reports describe a shorter sequence of recruiter, technical, and behavioral conversations.
Synthesized from 3 candidate reports by our editorial team.
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Featured question at Pinterest
Write a query to return whether each user's subscription date range overlaps with any other completed subscription
| Question | |
|---|---|
| Experiment Validity | |
| Search Ratings | |
| Button AB Test | |
| Impression Reach | |
| WAU vs Open Rates | |
| Size of Joins | |
| Ad Comments | |
| P-value to a Layman | |
| Random Bucketing | |
| Amateur Performance | |
| Feed Impression | |
| Priority Queue Using Linked List | |
| Dice Rolls From Continuous Uniform | |
| Banner Ad Strategy Success | |
| New UI Effect | |
| Greater Release Dates | |
| Most Repetition | |
| Interquartile Distance | |
| Max Width | |
| Unsafe Content ML Design | |
| A/B Testing a Checkout Button Change | |
| Overfit Avoidance | |
| Ranking Metrics | |
| Maximal Substring | |
| Reward Experiment | |
| Maximum Common Substring | |
| Singly Linked List | |
| Statistically Significant Test | |
| Video Pins |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates identified a recruiter screen as the first step. One recent account described a 15-minute recruiter meeting focused on logistics. Use this conversation to give a concise overview of your background and discuss availability; the reported substantive technical assessment followed later.
Candidates reported technical evaluation covering SQL, Python, statistics, and experiment design. One account described a 60-minute screen with two SQL, two Python, and two statistics or experimentation questions. Other reported prompts included pin popularity in SQL, dictionary-based pin similarity in Python, and A/B-test design.
Reported later-stage formats vary. One candidate described a two-day virtual onsite with six segments, including statistics, coding, a hiring-manager discussion, analytical problem solving, and cross-functional partnership evaluation. Another reported a final behavioral round after technical discussion. Prepare to explain analytical decisions clearly and discuss collaboration.
