Pinterest interview notes, page 24 of 24

Large Enterprises

Most of these Pinterest write-ups come from Machine Learning Engineers (91), then Software Engineers (77) and Data Scientists (51). The most reported rounds are the technical phone screen (168), the system design round (37), the product sense round (14) and the behavioral / leadership round (5).

235 interview notes · updated Sep 2026

Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest DS interview that was basically a gauntlet of SQL and pandas tasks built around a shopping engagement schema. No behavioral stuff, just pure technical work. The questions were detailed enough that I kept second-guessing whether I was overcomplicating things.

Write a single SQL query returning daily shopping engagement metrics for each date in a 7-day window, including DAU, click counts, average positive stay time, and a rolling 7-day unique user count. Deduplicate clicks within 5 minutes per user-pin pair using window functions, and use a generated dates CTE to fill in days with zero activity.
Compute next-day retention: among users who had at least one shopping click on 2025-08-31, what percentage also had a shopping click on 2025-09-01?
For the 7-day window ending 2025-09-01, return each user's top 2 pins by deduplicated shopping click count, with ties broken by total positive stay time and then by smallest pin ID.
In pandas, map product category codes to names using a provided dictionary so that any missing or unrecognized codes fall back to 'Unknown', replace negative stay_time_sec values with NaN, fill remaining NaN stay times with 0 for aggregation while still excluding zeros from averages, sort the DataFrame by user ID ascending then event timestamp and stay time descending, and finally compute each user's top product category by total positive stay time over the last 7 days with alphabetical tie-breaking.

“This one took me a while to structure.”

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Pinterest·Software Engineer·Technical Phone Screen

Jul 2026

Pinterest SWE interview with a binary search problem that looked like a scheduling question but was really just math dressed up fancy. One round, one meaty problem, lots of follow-ups.

You have a bank with k tellers, where teller i takes serviceTimes[i] minutes per customer. Given a target number of customers m, find the minimum time T such that the total customers served across all tellers is at least m. Walk through your algorithm, prove it's correct, analyze complexity, implement it, and cover edge cases like very large m, slow tellers, or identical service rates.

“Binary search on the answer, which I actually got to pretty fast.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest data scientist interview that was basically a pandas deep-dive. Four tasks, all coding, all interconnected, and the nested dict stuff at the end was where I started to feel the pressure.

Given a DataFrame where each row has a list of event dicts and a nested attrs dict, explode the events into a long-format table with one row per event, extracting fields like event_type, event_ts, amount, and nested attrs fields. No row-level for-loops allowed.
Aggregate the exploded event table into a per-user features table using groupby with named aggregations. For fields like version and beta flag, take the value associated with the user's most recent event.
From the per-user features table, compute a conversion rate by user segment defined by version, beta flag, and dark flag. Return one row per segment with columns for user count, purchaser count, and conversion rate.
Write a robust helper function that safely extracts nested keys from the attrs dict column using vectorized operations. Handle missing keys and None values, and explain in comments why your approach avoids SettingWithCopy issues and preserves vectorization.

“The explode part I got pretty fast.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest DS interview with a meaty product analytics case built around a homepage carousel launch that showed mixed signals in post-launch metrics. The whole thing felt like a real incident debrief more than a textbook question, which I appreciated but also wasn't fully ready for.

A new horizontal recommendations carousel was launched on the home page. Post-launch, the treatment group shows a drop in home-page CTR while app-wide DAU and total time spent stay flat. How would you define success metrics and guardrails for this surface?
Walk through a step-by-step diagnosis plan for why home-page CTR dropped while DAU and time spent remained unchanged. What logs or queries would you actually run?
What segmentation cuts would you use to localize the CTR drop? Name at least several specific dimensions.
Based on your diagnosis, what would you recommend as next steps? How do you decide between rolling back the feature versus iterating on it?

“I jumped straight to CTR and kind of forgot to think about what 'good' CTR even means for a carousel.”

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Pinterest·Software Engineer·Onsite - Multi Round

Jul 2026

Pinterest SDE 1 onsite across four rounds, coding heavy with a behavioral at the end. Two rounds felt solid, one was a mess due to a dumb debugging mistake, and the behavioral was more of a casual conversation than anything structured.

Coding problem with at least one follow-up question (Round 1).
Coding problem with two follow-ups, requiring edge case coverage (Round 2).
Hard algorithmic problem with multiple follow-ups (Round 3).
Behavioral interview covering leadership and past experiences (Behavioral Round).

“Went fine.”

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