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PayPal interview notes, page 1 of 10

Large Enterprises

Most of these PayPal write-ups come from Data Scientists (53), then Software Engineers (22) and Product Managers (10). The most reported rounds are the technical phone screen (60), the system design round (11), the product sense round (9) and the hiring manager screen (3).

91 interview notes · updated Jul 2026

PayPal·Data Scientist·Technical Phone Screen

Jan 2023
SQL screen for a Data Scientist role at PayPal. One question, interval overlap logic, felt straightforward on the surface but the self-join tripped me up for a minute.
  • You're given a sessions table with session_id, start_time, and end_time. Write a SQL query that returns the session with the most overlaps with other sessions, along with the overlap count.

“Took me longer than I'd like to admit to land on the self-join approach.”

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PayPal·Data Scientist·Technical Phone Screen

Jun 2024
PayPal data scientist interview with a coding-heavy technical screen focused on pandas and feature engineering for transaction data. Two related questions, both practical and grounded in real pipeline work.
  • Write a Python function that filters out users who have fewer than 100 transactions in a given calendar month.
  • Write a Python function that computes each user's average time between consecutive transactions, in seconds.

“I went straight to groupby and size() which was the right instinct, but I initially forgot to group by both user and month together.”

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PayPal·Data Scientist·Technical Phone Screen

Jun 2024
PayPal data scientist interview with a SQL question that looks straightforward until you actually sit down to write it. The sequence-detection angle tripped me up more than I expected.
  • Given a page_visits table with columns for date, user_id, page_id, and timestamp, write SQL to return distinct user_ids who visited page A followed immediately by page B on the same calendar day, where the page directly after B is NOT page C.

“I went for LEAD() pretty quickly, which was the right instinct, but I initially forgot to partition by both user_id and date together.”

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PayPal·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026
ML engineer system design round at PayPal, one question about building a recommendation system for Confluence pages. Pretty open-ended, which I wasn't fully prepared for.
  • Design a system that recommends the next Confluence pages a user should view.

“I jumped straight into collaborative filtering and probably spent too long there before they nudged me toward cold start.”

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PayPal·Software Engineer·Onsite - System Design / Architecture

Apr 2026
PayPal SWE interview with a UI design question focused on a price alert tracking feature. Pretty light on details but the prompt itself had some interesting surface area if you dug into it.
  • Design a price alert tracking UI for a payments or e-commerce context.

“I went straight to the visual layer and started sketching out components before really thinking about the data flow underneath.”

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PayPal·Data Scientist·Technical Phone Screen

Apr 2026
A PayPal DS interview question centered entirely on experiment design for a homepage feature. Dense prompt covering everything from hypothesis framing to ship decisions. No fluff, just a long technical case.
  • PayPal wants to launch a new homepage feature for logged-in users. Design an end-to-end experiment to decide whether to ship it, covering hypothesis, design, metrics, sample size, power, pitfalls, and the ship decision.

“This is basically a full DS case study crammed into one question.”

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PayPal·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PayPal PM interview with a single product design question around budgeting tools. Pretty open-ended, which sounds freeing until you're actually sitting there staring at a blank mental canvas.
  • Design a product that helps users manage and plan their budgets.

“My first instinct was to jump straight into features, which was probably the wrong move.”

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PayPal·Data Scientist·Technical Phone Screen

Apr 2026
PayPal data scientist interview that went deep into fraud detection, covering everything from raw transaction-level decisions to full system design and model evaluation. A lot of ground to cover in one session and I felt like I was scrambling by the third part.
  • You're given two credit card transactions with minimal information. Should each one be accepted or declined? Walk through your reasoning.
  • Design a complete fraud detection strategy for card transactions, covering what data you'd need, how you'd engineer features, what real-time rules you'd set, and how you'd layer in ML models.
  • How would you apply unsupervised learning to fraud detection? What algorithms would you consider?
  • What evaluation metrics would you use for a fraud detection model, and how do you evaluate an unsupervised model when you don't have ground truth labels?

“This felt like a trap but wasn't.”

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PayPal·Data Scientist·Technical Phone Screen

Apr 2026
PayPal data science interview focused heavily on A/B testing and product analytics. The main case was around a cashback program launch, which sounds straightforward but has a lot of moving parts if you actually try to work through it rigorously.
  • Walk me through how you'd design, run, and analyze an A/B test for a new checkout feature. Specifically: PayPal is launching a cashback program. How do you evaluate whether it's working, what metrics do you track, and what data do you need?

“This is a big question and I think I underestimated how much they wanted me to slow down on the hypothesis piece.”

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PayPal·Data Scientist·Technical Phone Screen

Apr 2026
PayPal data scientist interview with a probability and expected value problem centered on a dice game. The math wasn't brutal but the Monte Carlo follow-up added a wrinkle I wasn't fully expecting in a DS screen.
  • You and a house each roll one fair six-sided die. You win n dollars if your roll is strictly higher; you lose m dollars if the house ties or beats you. Derive the expected value of one round as a function of n and m.
  • Given that expected value formula, what condition on n and m means a risk-neutral player should actually play this game?
  • How would you validate that expected value using a Monte Carlo simulation in Python? Walk through the approach without writing production-level code.

“The probability of winning is 15/36 since there are 36 equally likely outcomes and 15 where your roll strictly beats theirs.”

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