Candidates describe a Pinterest Data Scientist process built around a recruiter call, a timed SQL, Python and statistics screen, and a virtual loop heavy on experimentation, product cases and cross-functional rounds.
$160K
Avg. Base Comp
$280K
Avg. Total Comp
Not reported
Typical Rounds
5 weeks
Process Length
Candidates describe the Pinterest Data Scientist interview as a mix of live technical work and product judgment. Several report a recruiter call followed by a roughly one-hour technical screen with SQL, Python and statistics or experiment-design questions. One candidate recalls two SQL, two Python and two stats questions, with window functions and a harder second SQL problem. Another describes a screen with two SQL questions, two Python questions and an experimental case study. Pacing is a recurring theme, so practice switching between topics quickly.
Experimentation is the most consistent theme. Candidates mention A/B test design, metric selection, sample size, long-term engagement, confounders, p-values, bootstrap sampling, causal inference, and skewed or zero-inflated metrics such as saves per user. Practice explaining why a metric could overstate a short-term win.
Later rounds appear broader than coding. Candidates describe an analytical problem-solving case, a hiring manager conversation, and a cross-functional partnership round. One recalls a feed-diversity measurement question and another a pin similarity coding task. Experiences differ in round count and format, and one candidate reports about five weeks end to end. Outcomes are mixed: several offers and two rejections, one citing timing and communication problems in the stats, product and coding rounds. Rehearse SQL and Pandas under a clock, and structure your product answers out loud.
Synthesized from 9 candidate reports by our editorial team.
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| Question | |
|---|---|
| Experiment Validity | |
| Button AB Test | |
| Search Ratings | |
| Impression Reach | |
| WAU vs Open Rates | |
| Size of Joins | |
| P-value to a Layman | |
| Ad Comments | |
| Random Bucketing | |
| Feed Impression | |
| Amateur Performance | |
| Priority Queue Using Linked List | |
| Dice Rolls From Continuous Uniform | |
| Banner Ad Strategy Success | |
| New UI Effect | |
| Most Repetition | |
| Greater Release Dates | |
| Interquartile Distance | |
| Unsafe Content ML Design | |
| Max Width | |
| A/B Testing a Checkout Button Change | |
| Overfit Avoidance | |
| Ranking Metrics | |
| Maximal Substring | |
| Reward Experiment | |
| Singly Linked List | |
| Maximum Common Substring | |
| Statistically Significant Test | |
| Video Pins |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter call covering background, role expectations and logistics. One candidate describes a short logistics-only meeting. It typically sets up the technical screen, so expect to describe your experience and why you want the role.
Candidates report a roughly one-hour live round covering SQL, Python or dataframe work, and statistics or experimentation. One recalls two questions in each area, including window functions. Others mention medium-level SQL and an A/B test design question. Pacing may matter as much as correctness.
Candidates describe statistics rounds on A/B test design, metric choice, sample size, p-values, bootstrap sampling and confounders. One reports a deep dive on causal inference and skewed experiment metrics. Expect interviewers to push beyond textbook answers and ask why a metric is trustworthy.
Some candidates report a further coding round with SQL, Pandas or Python, plus a case-style analytical problem such as diagnosing a metric or measuring feed diversity. These rounds may reward thinking aloud, committing to one approach, and managing time across several parts.
Candidates report a hiring manager conversation, behavioral questions, and in some loops a cross-functional partnership round on product intuition, stakeholders and prioritization. Round order and count vary between reports, so prepare concrete stories about influencing decisions with data.
