Want to get into PayPal Such a leading global online payment platform is no easy task. There are four rounds of interviews, covering technical skills, business analysis, logical reasoning and communication. In this article, I'd like to share with you the PayPal interview experience I've compiled + ideas for solving the questions, in the hope that it will help students who are preparing to take a detour and prepare for the battle in a more targeted manner.
Round 1: Coding
2 Python questions + 3 SQL questions
Focus:
- Python: basic data structures (dictionaries) and loops
- SQL: Session Overlap Analysis Based on
start_timeandend_time
Coding 1 - Finding the sum of two transactions
Given an array of transactions and a target amount target, find two transactions whose sum equals the target amount and return their indexes.
transactions = [12.50, 7.35, 6.15, 6.50, 8.50]
target = 13.65
# Output: [1, 2] (7.35 + 6.30 = 13.65)Code 2 - High Frequency Transaction Detection
Given an array of transactions and a target amount target, find two transactions whose sum equals the target amount and return their indexes.
logs = [
"txn001,2025-04-15 10:00:00",
"txn002,2025-04-15 10:00:00",
"txn003,2025-04-15 10:00:00", "txn004,2025-04-15 10:00:00".
"txn004,2025-04-15 10:00:00", "txn004,2025-04-15 10:00:00".
"txn004,2025-04-15 10:00:01"
]# Output: [["txn001", "txn002", "txn003"]]
Round 2: A/B testing
- Design, traffic segmentation, metrics analysis
- Statistics: e.g., number of outcomes for 10 coin flips is 210 = 1024
Round 3: Case studies
Round 3: Case studies
The interviewer in this round was an old PayPal guy who has been in the business for 11 years and has basically seen all kinds of user behavior. He threw a pretty realistic question at me right off the bat:
"If we were to make a cashback program that would drive up user activity and transactions, but not be bald by the woolgatherers, how would you design it?"
My thinking at the time was to break down the problem first. Cashback can really attract people, but the money can not be blindly smashed down, so I said to first do a small-scale experiment (A/B Test), to see the impact of the strength of the cashback on the transaction frequency, GMV, retention rate of these core indicators.
Then I added a point: cashback program will definitely attract woolly party, so how to identify it? I will set some rules, such as frequent opening of small numbers in a short period of time, only the minimum consumption of users, to do screening.
Finally, I added a "budget is limited", so we have to do dynamic optimization on the ROI (input-output ratio), the cashback amount can be stratified, such as old users and new users are different, different transaction amount of cashback ratio is also different.
Round 4: Behavioral Interview
This round had a different vibe, more like a chat. He asked me to tell him about a project from my past experience, preferably one that was more challenging and demonstrated problem solving skills.
I shared a clinical data project I did before. The hospital gave us a bunch of case data and asked us to analyze the effect of a certain treatment plan. The problem was that the quality of the data was very poor, there were a lot of missing values, and the definitions of the variables were not standardized.
I started with the Situation: the data was a mess and the team kind of fell apart at one point.
Then comes the Task: I have to clean a version of the dataset that works in two weeks, and I also have to make sure that the conclusions are reliable.
Action: I led the team to unify the caliber of variables, and deal with the missing values in different situations, some with interpolation, and some directly discarded; at the same time, I pulled the clinicians for a meeting to confirm the medical significance of the key indicators, so as to avoid filling in the data "in our heads".
Final result (Result): The data set delivered two weeks later was recognized by the doctors, and the effect analysis we ran successfully supported the hospital's decision-making.
ProgramHelp helps you get an offer.
When preparing for interviews at big companies like PayPal, many students are torn: should they brush up on their questions? In fact, from this experience, we can see that brushing more questions will really pay off, especially in the OA and algorithm sessions, a solid foundation can make you in the subsequent rounds of interviews much easier.
Of course, clinical play is just as critical. Our team has long provided OA Writing Guarantee(HackerRank, CodeSignal, Cowboys, etc. full coverage), remote interview assistance (real-time voice/prompts, leaving no traces), as well as the whole process of the interview accompaniment, to help you in the critical moment less stepping on the pit, and successfully pass the test.
If you're also preparing for an interview at PayPal or any other top tech company, feel free to reach out to us and we'll help you punch through in the surest way possible 🚀.
