Paypal Interview Questions
Practice 96 real interview questions from Paypal. Covers Analytics & Experimentation, Coding & Algorithms, Data Manipulation (SQL/Python), Statistics & Math. For roles including Data Scientist, Software Engineer, Machine Learning Engineer.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
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"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Design a Payment Fraud Detection Service
Design a fraud detection service for a payment platform. The service should evaluate payment attempts in real time and return a decision such as allow...
Design a Cross-Border Money Transfer Service
Design a cross-border money transfer service similar to a consumer remittance product. Users in one country should be able to send money to recipients...
Minimize a String Using Allowed Swaps
You are given a string s of lowercase English letters and an array pairs, where each element pairs[i] = [a, b] means you may swap the characters at in...
How would you measure impact?
A payments company launches a new fraud-screening feature that adds an extra risk check before approving certain transactions. Leadership wants to kno...
How to validate production models?
You are interviewing for a fintech model-validation team that acts as a second line of defense for credit-risk and fraud models. A hiring manager asks...
Write SQL using HAVING and window functions
Context You work on fraud analytics. Assume the following schema (PostgreSQL-like types): transactions - txn_id BIGINT (PK) - merchant_id BIGINT - use...
Explain fraud types and evaluate a fraud model
You are interviewing for a Fraud Data Scientist role at PayPal. Answer the following: 1) List common fraud types relevant to payments (e.g., account t...
Explain list vs tuple in Python
Question In Python: 1. What are the key differences between a list and a tuple? 2. When would you prefer using a tuple over a list? 3. What are the pe...
How to evaluate a new homepage feature
Scenario PayPal plans to launch a new homepage feature (e.g., a new CTA module, personalized content, or a redesigned layout). You are asked to evalua...
Compute variance of a list in Python
Task Given a Python list of numbers (ints/floats), write code to compute its variance. Requirements - Input: nums: list[float] (length \(n\ge 1\)) - C...
Design a traditional fraud detection system
Design an End-to-End Real-Time Payments Fraud Detection System Context: You are designing a fraud detection system for a large-scale online payments p...
Why does the CLT matter?
Explain the Central Limit Theorem (CLT) and why it matters in data science and machine learning interviews. Then show how you would use the CLT to bui...
Design a fraud mitigation strategy under constraints
You are given a one-page case during a hiring manager round for a Fraud Data Scientist role. Current state: - The existing fraud model is performing p...
Design metrics and experiment for donation feature
Product/Experimentation Case Uber Eats is considering a new feature: when a user places an order, they can optionally donate (tip-like or charitable d...
Solve common search/parse/graph frequency tasks
You are given several independent coding tasks. For each task, write a function that returns the required output. 1) Find insertion index in a sorted ...
Explain and interpret p-values correctly
Context You are evaluating a change to a fraud decision rule (e.g., a new threshold or step-up authentication rule). You run an experiment comparing C...
Write SQL for top drivers and cancellation rates
You work on a rideshare product with airport pickups. Using SQL, answer the questions below. Assume all timestamps are stored in UTC. Define the analy...
Explain confounding with an Uber example
You are interviewing for a Senior Data Scientist role at Uber. 1) Define confounding in the context of estimating causal effects from observational da...
Compare write-back vs write-through caches
Compare write-back and write-through caching policies. Explain how each handles writes, coherence, durability, latency, and bandwidth; discuss typical...
Implement sliding-window device anomaly
Streaming detection algorithm: Implement a function process_logins(stream) that consumes a time-ordered stream of login events (user_id, ts, device_id...