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PayPal interview notes, page 3 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

May 2026
PayPal payments-risk case for a data scientist role. The whole thing was one big scenario question about real-time fraud decisioning, which I was not fully prepared for in terms of how deep they wanted to go on the business trade-offs.
  • You're on the payments-risk team and two new transactions come in with attributes like amount, issuing country, device type, historical fraud rate, and card age. Walk through your real-time decision process for each transaction, name at least three risk strategies you'd deploy right away, and explain the trade-offs between false positives and chargebacks.

“I started with the obvious stuff, velocity checks and rule-based thresholds, but then kind of froze when they pushed on how I'd actually set those thresholds.”

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PayPal·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a business analyst role on PayPal's core payments team. One case question, focused on diagnosing a YoY drop in transaction volume across North America. Pretty open-ended, which I wasn't fully prepared for.
  • Transaction volume across North America has been declining year-over-year. As part of the core payments analytics team, how would you structure your investigation into what's driving this?

“I froze a little because it's broad enough that you could go in a dozen directions.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
PayPal data science interview with a meaty A/B testing case built around a real product scenario. The whole thing was one extended design question and they went pretty deep on the stats side, more than I expected.
  • Design an A/B test to demonstrate that PayPal cashback creates measurable value for Walmart. How would you structure the experiment end to end?
  • What are your primary, secondary, and guardrail metrics for this experiment, and why?
  • Walk through your power analysis approach and explain how you'd interpret the p-value from the results.
  • If the experiment results fall below the minimum detectable effect, what would you do next?
  • The experiment is underpowered but you can't extend the timeline. What alternatives exist to still get a valid result?

“This felt manageable at first and then kept expanding.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
PayPal data scientist interview with a live SQL session on a transactions table. Nothing too crazy but the HAVING clause question tripped me up a bit in the moment.
  • Given a transactions table, write a query that returns each user_id along with the total amount from their successful transactions only.
  • Add a classification column to the transactions table that labels each row as 'small', 'medium', or 'large' based on the transaction amount, using a CASE WHEN expression.
  • Write a query to find users whose total successful transaction amount is greater than 1,000, using a HAVING clause rather than filtering after the fact.

“Pretty standard warm-up.”

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PayPal·Software Engineer·Technical Phone Screen

May 2026
PayPal coding screen, pretty much just one graph problem. Nothing fancy, no behavioral, just get the answer and move on.
  • Given an n x n adjacency matrix representing direct connections between cities, return the total number of connected components (provinces) in the graph.

“Classic union-find or DFS problem.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
First-round Python screen for a Data Scientist role at PayPal. Pretty straightforward coding task but I overthought parts of it and the sorting tripped me up a bit.
  • Write a function in plain Python (no libraries) that takes a list of strings and returns a dictionary of each unique word mapped to how often it appears. Then print the top three most frequent words in descending order.

“Felt fine building the frequency dict with a for-loop, that part came naturally.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
PayPal data scientist interview with a live Python coding exercise where they had you write bigram generation from scratch and then keep refactoring it down to a one-liner. Pretty focused session, no fluff.
  • Write a Python function that takes a sentence and returns all consecutive word bigrams as a list, then refactor it step by step into a list comprehension and finally a single line using zip.

“Started fine with the naive loop version but then they asked me to compress it further and I kind of stalled on the one-liner.”

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PayPal·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a software engineering role at PayPal and got hit with a pretty deep C++ memory management question. Not the easiest topic to cover under pressure, but it was a good signal about what they care about at this company.
  • How do you detect and prevent memory leaks in C++? Walk through the tools you'd use and the design practices that help avoid leaks in the first place.

“I started with Valgrind/Memcheck since that's the one I've actually used in production and felt confident talking through.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
Technical phone screen for a Data Scientist role at PayPal. One Python fundamentals question that sounds easy but has more layers than you'd expect if you haven't thought about it carefully in a while.
  • What are the key differences between a list and a tuple in Python? Walk through mutability, performance, typical use cases, and when a tuple can be used as a dictionary key.

“I covered mutability fine and even threw in a quick code snippet showing you can't reassign a tuple element.”

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PayPal·Data Scientist·Technical Phone Screen

May 2026
PayPal data scientist screen, SQL and Python heavy. They gave you a transactions table and worked through a bunch of concepts back to back, felt more like a technical quiz than a real conversation.
  • What are window functions and what are some common use cases for them?
  • Explain the difference between INNER, LEFT, RIGHT, FULL OUTER, and CROSS joins.
  • What is the difference between RANK() and DENSE_RANK()?
  • Write a SQL query that calculates total GMV grouped by merchant.
  • Write a SQL query that returns each user's list of merchants as a single comma-separated string, handling any type casting between DECIMAL and STRING where needed.
  • Using Python, iterate over a list of dictionaries representing transaction rows and build a mapping from user_id to their total transaction amount.

“Went fine, covered running totals, ranking rows within partitions, lag/lead for time-series comparisons.”

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