PayPal is an American digital payment platform that boasts over 200 million online users globally. The company searches for data scientists to examine transactional data and user behavior, enhancing user experience, identifying fraud, and promoting business growth.
This guide offers a summary of PayPal’s data science interview questions and their hiring process for data scientists. For comprehensive preparation to tackle data science interview questions, explore the data science learning path.
Data science interviews at PayPal typically span one to two weeks and encompass four interview stages: a technical screening, a virtual interview with an HR representative, and two on-site interviews.
The process may incorporate a take-home assignment relevant to the role’s responsibilities, depending on the specific position.
Data science interviews at PayPal cover a broad range of topics, the most common being SQL, Python, algorithms, and machine-learning questions. Probability questions are more common at PayPal than in the average data science interview. Let’s look at some example questions from PayPal for each of these topics.
SQL questions appear in almost every PayPal data science interview, given the company’s reliance on relational databases for managing and analyzing transactional data. To help you prepare, we’ve provided a few example questions below:
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. How would you do this
Two tables are available: swipes
, which includes a boolean column is_right_swipe
, and variants
, which contains AB test data. Construct a SQL query to determine the average right swipes for users who swiped a minimum of 10 times, comparing their initial 10, 50, and 100 swipes.
There is a users
table with user information and a purchases
table documenting purchase history. Create a query to find the total amount spent on every item by users who registered in 2022.
As you prepare for the interview, consider reviewing the complete collection of SQL questions for data scientists and the SQL learning path.
At PayPal, algorithm-related job interviews evaluate candidates’ understanding of data structures and algorithmic efficiency to gauge their ability to handle large datasets and inputs effectively. They also tend to evaluate candidates’ string manipulation skills for data cleaning.
max_repeating
to return which character has the longest string of continuous repetition.Develop an algorithm max_repeating
that identifies the character with the longest continuous repetition in a given string str
.
fund_return
to calculate the total profit gained from investing in the index from the start to end date.Create a function fund_return
to compute the total profit from investing in an index fund using deposit, withdrawal, and daily price data.
rearranging_digits
to return a string of the smallest number larger than n that can be created by rearranging the digits in n.Formulate a function rearranging_digits
that returns the smallest number greater than n
by rearranging the digits in the input string n
.
Design a function to pair people based on availability and similar interests, prioritizing the total number of matches, followed by matching according to interests. Return a list of matches with scheduled times and a separate list for unmatched individuals.
To further your practice, we have prepared an in-depth list of Python data science interview questions. Additionally, consider using the Python learning path to review essential concepts for your data science interview.
Case study questions assess your ability to apply technical knowledge to business problems. At PayPal, case studies often involve creating, developing, or evaluating payment and banking systems, as well as addressing typical business case studies, such as product metrics or marketing analytics questions. Here are some examples:
How would you design an A/B test to determine if changing a button’s color and/or its position on a page increases click-through rates?
With an A/B test having unequal sample sizes of 50K and 200K users, can you determine if the test will be biased towards the smaller group?
If weekly active users increased by 5% while email notification open rates decreased by 2%, what factors would you investigate to diagnose the situation?
What are the pros and cons of user-tied tests, where users are randomized into groups, compared to user-untied tests, where instances are randomized?
To prepare for PayPal’s case studies, consider resources like the A/B testing and statistics learning path, statistics and A/B testing interview questions article, product metrics learning path, and the product metrics interview questions article. Additionally, explore our articles on case study questions tailored to roles like data scientists.
PayPal’s machine learning interview questions primarily focus on the payments and financial sectors, covering topics such as fraud detection, risk assessment, and personalized recommendations.
Additionally, these questions assess your understanding of fundamental machine learning concepts and your ability to apply these techniques to address a range of business challenges.
Here are some example questions you might come across during a PayPal interview:
PCA is a dimensionality reduction technique that reduces variables, and K-means is a clustering algorithm that partitions data into K clusters.
PCA can be used as a preprocessing step to reduce noise and improve the performance of K-means, especially when dealing with high-dimensional data.
To build a binary classifier on an imbalanced dataset, several considerations need to be made. These include assessing the accuracy and labeling of the data, choosing an appropriate model, considering interpretability, and evaluating the costs of misclassification.
In the case of a text messaging service, optimizing for recall to minimize risk and avoid costly fraudulent charges may be preferable.
Given a dataset of 600,000 credit card transactions, how would you create a fraud detection model for a major credit card company?
What machine learning methods would you use to build a chatbot system that matches user questions with the closest answers from a list of frequently asked questions?
To prepare for machine learning interview questions, consider taking the machine learning course. Additionally, practice with this list of fintech machine learning projects to familiarize yourself with PayPal’s domain and add a relevant project to your portfolio.
Probability questions play a vital role in evaluating a candidate’s problem-solving abilities, critical thinking skills, and attention to detail. For data-related roles, a firm understanding of probability is necessary to manage uncertainty effectively when making predictions.
Below are some example probability questions you might come across during the PayPal interview:
You flip a coin 10 times, and it lands on tails 8 times and heads twice. Can you determine if this is a fair coin?
In a coin flip game, you start with 30 dollars. Winning a flip (heads) earns you a dollar while losing (tails) costs you a dollar. The game continues until you either run out of money or win 100 dollars. What is the probability of winning 100 dollars?
Given an unfair coin with an unequal probability of heads and tails, can you design an algorithm that generates a list of uniformly distributed zeros and ones only using the results of the coin tosses?
Given that X and Y are independent random variables with normal distributions, what is the mean and variance of the distribution of 2X - Y when the corresponding distributions are X ~ N(3, 4) and Y ~ N(1, 4)?
Considering that 98% of reviews are legitimate and 2% are fake, a machine learning algorithm identifies fake reviews with 95% accuracy and legitimate reviews with 90% accuracy. What is the likelihood that a review is actually fake when the algorithm flags it as such?
To prepare thoroughly for probability interview questions, check out the comprehensive probability learning path. It covers basic to advanced probability concepts, including multivariate distributions and sampling theorems.
Behavioral questions serve to evaluate a candidate’s interpersonal skills, such as communication, problem-solving, teamwork, and leadership abilities. These questions help interviewers gauge how well a candidate would fit within the company culture and team dynamics.
Here are a couple of behavioral questions you might be asked during a PayPal Data Science interview:
This kind of question comes in many forms, such as: Why did you apply to our company? What are you looking for in your next job? What makes you a good fit for our company? It’s important to be able to articulate your motivations and demonstrate your understanding of the company.
This is another behavioral question interviewers come to ask. Other formulations could be: What would your current manager say about you? What constructive criticisms might he give? What are your three biggest strengths and weaknesses you have identified in yourself?
Knowing how to respond to these questions could be the deciding factor in interviewers choosing you over another candidate for the role.
To prepare for behavioral questions in data-related roles, consult the articles on data scientist behavioral interview questions and data analyst behavioral interview questions for helpful tips and guidance.
Here’s an updated list of the most recently asked questions reported for PayPal: