PayPal is a leading American digital payment platform with over 200 million online users worldwide. It seeks data-related roles to analyze transactional data and user behavior. This helps enhance services, detect fraud, and boost business growth.
The PayPal interview process tends to last between one and two weeks and consists of four rounds of interviews: a technical screening, a remote interview with an HR representative focusing on behavioral aspects, and two on-site interviews with members of the teams you’re applying for that go for an in-depth evaluation on several topics.
This guide offers valuable insights into the PayPal interview process and common questions for technical roles. For specific positions at PayPal, consult the appropriate interview guide above.
PayPal uses SQL to manage and analyze data. So, SQL questions often appear in interviews for data-related jobs like data analysts, data scientists, and data engineers. Here are a few SQL questions from PayPal interviews:
You have a
transactions table with details like
user_id and transaction date. Your task is to create a new table that shows the third purchase for each user.
There are two tables:
swipes with a boolean column
variants with AB test data. Write a SQL query to find the average right swipes for users who swiped at least 10 times. Compare their first 10, 50, and 100 swipes.
You have a
users table with user info and a
purchases table with purchase history. Write a query to find the total amount spent on each item by users who registered in 2022.
PayPal evaluates candidates’ skills in data manipulation, problem-solving, and optimization for large-scale datasets through algorithm questions. Coding questions are typical for data-related roles, including data scientists, data engineers, and machine learning engineers.
These questions focus on string manipulation and algorithm efficiency—crucial skills for data cleaning and effective data handling.
Create a function to check if a string is a palindrome—a word that reads the same forwards and backward (e.g., ‘reviver,’ ‘madam,’ ‘deified,’ ‘civic’).
Given a list of float numbers representing client spending, write a function that finds the threshold to separate high spending vs. low-spending customers for a company’s credit card acquisition strategies.
Design a function to match people based on availability and similar interests. Optimize for the total number of matches first, then for matching based on interests. Return a list of matches with scheduled times and a separate list for unmatched individuals.
max_repeatingto find the character with the longest continuous repetition in a string.
Given a string str of any length, write an algorithm
max_repeating to return which character has the longest string of continuous repetition.
If two characters are tied for the most continuous repetition, return the character whose longest continuous repetition occurs earliest in str.
str_mapto determine if a one-to-one correspondence exists between characters of two strings at the same positions.
Given two strings, string1, and string2, write a function
str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.
For the two strings, our correspondence must be between characters in the same position/index.
Candidates may also encounter questions testing proficiency with Python libraries for data manipulation and visualization, such as Pandas, Numpy, or Matplotilb. To practice Python interview questions, consider using the Python learning path or the full list of Python questions and algorithms & data structures questions in our database.
Case study questions test your ability to use technical knowledge to solve business issues. PayPal’s case studies often center on creating, developing, or evaluating payment and banking systems. Here are some examples that have either been used in PayPal job interviews or are similar to those asked by PayPal in the past:
The system monitors credit card activities and flags a transaction as suspicious if it happened in a place other than where a person lived.
For that system to work, you need to identify where a person lives based on their credit card activity. How would you infer a customer’s location from their purchases?
A team wants to A/B test various changes in a sign-up funnel. For instance, on a page, a button is red and at the top. They want to see if changing the button’s color to blue and/or moving it to the bottom will increase click-through rates. How would you set up this test?
Robinhood experimented with sending push notifications to active users when the market opened. Analyze the experiment results, determine significant metrics, and decide if the company should implement these notifications for all users. Explain your decision.
Suppose you need to analyze an A/B test’s results. One variant has a sample size of 50K users, while the other has 200K users. Can you identify if the test will be biased towards the smaller group due to the uneven sizes?
Some resources you can use when practicing for PayPal’s case studies are the A/B testing and statistics learning path and our in-depth article on statistics and A/B testing interview questions, as well as the product metrics learning path and our in-depth article on product metrics interview questions.
In addition, we’ve written some articles on case study questions tailored to different roles, including data scientists, data analysts, business intelligence analysts, and data engineers. These articles can provide more in-depth guidance on how to approach case study questions specific to your role.
As a financial technology leader, PayPal’s machine learning questions often revolve around the payments and financial sectors, with a focus on fraud detection, risk assessment, and personalized recommendations. They also test your understanding of general machine-learning concepts.
Here are some example questions you might come across during a PayPal interview:
Imagine creating a chatbot for frequently asked questions. When a user submits a question, the chatbot should return the closest matching answer from a list of FAQs. What machine learning methods would you consider for building this system?
Suppose you work at a bank aiming to develop a fraud detection model. The bank also plans to implement a text messaging service that notifies customers of potentially fraudulent transactions and allows them to approve or deny the transaction via text. How would you approach building this model?
Imagine working at a major credit card company with a dataset of 600,000 transactions. How would you use this data to create a fraud detection model?
Assume you have a credit model that estimates individuals’ creditworthiness with a small margin of error.
If the model’s estimate is 83%, the actual score likely falls between 81% and 85%. By using 83% as a cutoff for creditworthiness, are we overestimating or underestimating the actual credit scores of the population?
Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not. What metrics would you track to measure the success of the new model?
To get ready for machine learning interview questions, we recommend taking the machine learning course. It will provide you with the knowledge and practice you need to tackle various machine-learning problems, ensuring you are well-prepared for your interview at any company in the financial technology space.
PayPal’s interview questions on database design often revolve around creating, optimizing, and managing strong payment and banking systems. Candidates should be prepared to handle topics such as efficient schemas, queries, large-scale data structures, data modeling, normalization, indexing, and data warehousing.
Here are a few example questions you might encounter during a PayPal interview:
Imagine you need to set up analytics tracking for a web app. How would you design a schema to represent client click data on the web?
Suppose you have analytics data stored in a data lake, and an analyst requests hourly, daily, and weekly active user data for a dashboard that refreshes every hour. How would you construct this data pipeline?
If you have a table with a billion rows, how would you add a column with data from the original source without negatively impacting the user experience?
What are the essential functional and non-functional requirements for a database system that stores API keys and related information for a company providing payment management services to software developers?
Assume you’re responsible for incorporating payment data into your internal data warehouse. How would you build an ETL pipeline to transfer Stripe payment data into the database, enabling analysts to create revenue dashboards and perform analytics?
To further prepare for database design interview questions, check out the database design interview questions article and the course on database system design. These resources can help you learn the framework for answering such questions effectively.
Probability questions can help gauge a candidate’s problem-solving abilities, critical thinking skills, and attention to detail. In data-related roles, understanding probability is essential for accurately handling uncertainty when making predictions.
Here are some example probability questions you might encounter during an interview:
Imagine you draw two cards from a shuffled deck, one after the other. What is the probability that the second card is not an Ace?
You have a fair coin and flip it until either Heads Heads Tails (HHT) or Heads Tails Tails (HTT) appears. Is one sequence more likely to appear first? If so, which one and with what probability?
Given that X and Y are independent random variables with normal distributions, determine the mean and variance of the distribution of 2X - Y when the corresponding distributions are X ~ N(3, 4) and Y ~ N(1, 4).
Suppose you want to identify fake reviews for your products. From past data, you know that 98% of reviews are legitimate, and 2% are fake. If a review is fake, there’s a 95% chance that the machine learning algorithm labels it as fake.
If a review is legitimate, there’s a 90% chance that the algorithm labels it as legitimate. What is the percentage chance that a review is actually fake when the algorithm identifies it as fake?
To prepare for probability interview questions fully, check out the comprehensive probability learning path. It covers basic to advanced probability concepts, including multivariate distributions and sampling theorems.
Behavioral questions aim to evaluate a candidate’s soft skills, such as communication, problem-solving, teamwork, and leadership. Here are a couple of behavioral questions you might be asked in the PayPal interview:
Share an instance when your colleagues disagreed with your approach. How did you involve them in the conversation and address their concerns?
Describe a time when you went above and beyond expectations during a project. What actions did you take, and how did you achieve this?
To prepare for behavioral questions in data-related roles, explore the articles on data scientist behavioral interview questions and data analyst behavioral interview questions for useful tips and guidance.
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Here’s an updated list of the most recently asked questions reported for PayPal: