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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Real interview reports from people who went through the Pinterest process.
My Pinterest Data Scientist technical screen was a compact 60-minute round that covered a lot of ground. The interviewer moved through SQL, Python, and statistics or experiment design in the same session, so the hardest part was pacing. There was not much room to get stuck on any one section because the round included two SQL questions, two Python questions, and two stats or experimentation questions.
The SQL portion stood out the most. The questions were medium to hard, and at least one focused on window functions. The first SQL question felt manageable if you were comfortable with standard analytics patterns, but the second was noticeably harder and required being careful with the query structure. The Python questions were also part of the screen, but the overall impression was that the interview was testing practical data science fluency across several areas rather than one deep algorithmic trick.
My takeaway is that candidates should practice moving quickly between SQL, Python, and experiment-design reasoning. For this round, knowing the concepts is not enough; you need to be able to execute under time pressure and explain your approach clearly while switching topics.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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.
