Pinterest Data Scientist interview typically runs 3 rounds: recruiter screen, technical screen, final behavioral. It usually takes about 1-2 weeks and is notably sharp and well-run, with a heavy emphasis on experiment design.
$140K
Avg. Base Comp
$385K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
Our candidates consistently describe Pinterest as a company that cares less about flashy theory and more about whether you can reason cleanly about product impact. The strongest signal is how often the conversation comes back to A/B testing: one candidate said the interviewer kept pressing on long-term engagement, confounders, and metric choice, which tells us Pinterest wants people who can defend an experiment beyond the obvious lift. It’s not enough to say a test “worked” — they want to know whether it changed behavior in a way that actually fits the product.
A recurring theme is the breadth of the technical screen. Multiple candidates reported being asked to move quickly across SQL, Python, and statistics/experimentation in the same session, with SQL often feeling the most demanding. We’ve seen mentions of window functions and harder second queries, which suggests the bar is practical fluency under time pressure, not just familiarity with syntax. The non-obvious trap here is pacing: candidates who can explain their approach clearly while switching topics seem to do better than those who get bogged down trying to perfect one answer.
What stands out overall is that Pinterest appears to value candidates who think like product scientists. The process rewards people who can connect a metric choice to user behavior, anticipate how a short-term win might mislead the team, and stay crisp when the interviewer keeps pushing. In our view, that combination — experiment rigor plus calm execution — is what most often separates a solid interview from an offer-level one here.
Synthetized from 2 candidates reports by our editorial team.
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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 | |
|---|---|
| Button AB Test | |
| Search Ratings | |
| WAU vs Open Rates | |
| Random Bucketing | |
| Amateur Performance | |
| P-value to a Layman | |
| Ad Comments | |
| Feed Impression | |
| Priority Queue Using Linked List | |
| Dice Rolls From Continuous Uniform | |
| Unsafe Content ML Design | |
| Interquartile Distance | |
| Greater Release Dates | |
| Max Width | |
| Most Repetition | |
| A/B Testing a Checkout Button Change | |
| Overfit Avoidance | |
| Maximal Substring | |
| Maximum Common Substring | |
| Singly Linked List | |
| Video Pins | |
| Statistically Significant Test | |
| Meaningful Session Calculation | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Rolling Bank Transactions |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with a recruiter screen to review your background, interest in Pinterest, and overall fit for the Data Scientist role. This stage appears to be a standard first contact before moving into technical interviews.
This round is a fast-paced technical screen covering SQL, Python, and statistics or experiment design in the same session. Candidates reported two SQL questions, two Python questions, and two stats/experimentation questions, with SQL including medium-to-hard problems such as window functions.
A deeper technical round focuses on end-to-end A/B test design, including how to choose metrics, reason about sample size, and account for confounders and long-term engagement. Interviewers push on the details, so candidates need to explain not just the framework but why specific metrics and assumptions matter.
The final round is behavioral and lighter than the technical interviews. It typically covers collaboration, communication, and general fit, serving as the last step before the hiring decision.