Paypal Data Science Interview Questions

Paypal has 254 Million+ active customer accounts doing 2.7 Billion+ payment transactions worldwide.

Paypal was founded by luminaries of our generation including Elon Musk in 1998. Paypal Mafia (Early Paypal employees and cofounders) are credited with a huge amount of innovation in the Silicon Valley in the past decade. With 13.09 Billion $ in revenue in 2017 and transacting in over 100 currencies, one can imagine the scale of the infrastructure and data inside Paypal. Much of modern day online currency transactions which are common place today were done by Paypal since the last 20 years when no one else did it. Paypal data is a trove for Data Scientists to learn and grow from.

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Interview Process

Their interview process consists of a Codility coding challenge as a screening interview. After the first screening interview there is an onsite interview consisting of interviews with the actual team members. The onsite interviews concentrate on your resume and projects in the Data Science domain. The questions usually start on those topics expanding to different topics which might be important for Paypal. Huge component of being a payment transaction based company is being very good at fraud detection. That topic is surely comes up in the interview. Paypal has over 160+ PB Data and hence, data wrangling is also something which comes up frequently.

Important Reading
Paypal’s Data Engineering Architecture. Source: Paypal Blog
AI/Data Science Related Questions
  • What’s the Central Limit Theorem, and how do you prove it? What are its applications?
  • Write a function that takes a sentence and prints out the same sentence with each word backwards in O(n) time.
  • Write a function that takes an array, splits the array into every possible set of two arrays, and prints out the max differences between the two array’s minima in O(n) time.
  • Write a program that does merge sort.
  • How did you consider overfit/ variable selection in this project(project present on the resume)?
  • Given a corpus of PayPal transactions — name, email, ip, sum, product and other attributes how will you be able to discern legitimate transactions from the illegitimate transactions?
  • If you know for certain that a friend of yours has two children and that at least one of them is a boy, what is the probability that the other is also a boy?
  • If you merge two datasets in SAS without BY statement, how would the resultant output be?
  • How many pencils are used in India?
Reflecting on the Questions

The data science team at Paypal is one of the most seasoned team in the industry to work for. The team hires data scientists in multiple locations in the world and has been around since even before Data Science was big. If a Data Scientist wants to grow his career in the fintech space, Paypal fits the bill. A good background knowledge about fintech and great amount of hard work will land you in a job at one of the biggest online transaction companies in the world.

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The sole motivation of this blog article is to learn about Paypal and its technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.