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PayPal Data Scientist
The role of a PayPal Data Scientist
PayPal is one of the largest online payment processors in the world. From fraud detection to product analytics and loss forecasting models, everything at PayPal is data-driven. This is the reason the company is looking to onboard the smartest data scientists to help it grow as a financial services company with technological innovations and data-driven decision making. As a Data Scientist, you will evaluate and improve PayPal's products and services. You'll collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems, and brainstorm solutions to the challenges of measuring quality, preparing models, and improving consumer services in areas such as fraud detection, risk forecasting, consumer analytics and more.
Data Scientist average salary at PayPal:
- Entry-level salary : USD 116,000
- Senior positions :USD 210,000.
- Median salary: USD 160,000 with the base component being USD 100,000, stock component being USD 40,000 and bonus being USD 20,000.
Roles and responsibilities
PayPal has a variety of Data Science teams that handle various aspects of the company's data ecosystem, and your exact role may depend significantly on which team you're in.
Here are some of the Data Science teams at PayPal:
- Fraud Detection
- Data Technology
- Loss Forecasting
- Customer Support Intelligence
- Merchant & Consumer Analytics
Although each of these roles entails specific responsibilities, a data scientist at PayPal, across the board, is expected to carry out responsibilities such as problem structuring, data preparation, model & strategy building, model & strategy validation, benefits measurement, and more.
Preferred Skills and Qualifications
- Bachelor’s degree in a quantitative or computer science or engineering field with 4+ years professional work experience in a relevant field. Or a Master's degree with 2+ years of professional experience.
- Industry experience in payments, e-commerce, or financial services is a strong plus
- Strong in data science, analytics and problem-solving. Experienced with predictive modelling, data mining and analytical tools.
- Proficient with SQL, Python, SAS, etc., familiar with Unix.
- Proven experience in employing analytics solutions to real-world problems.
- Good business sense and logical thinking. Ability to synthesize information and generalize the pattern.
- Proven ability to work independently and proficiently on well-defined projects; Focuses primarily on how to achieve overall business objectives of a project with speed and quality
- Effective interpersonal skills. Manage relationships with key partners across different functions. Able to collaborate with remote teams effectively.
- Skilled in Presenting – can effectively communicate in large or small group settings.
PayPal Data Scientist Interview Guide
Once the candidate's resume is shortlisted, the interview process begins, broadly consisting of 2 stages:
- Phone Screen
- Onsite round
Phone Screen
Overview
The Phone Screen is conducted by the Talent Acquisition and Business teams at PayPal.
As the first step, the Talent Acquisition team explains the roles and responsibilities to the candidate, and tries to assess the cultural and experiential fit of the candidate for the role. This interview takes about 20-25 minutes.
This is followed by an interview with the Business team. The Business team spends about 30-45 minutes explaining the roadmap, challenges and business goals of the company. They attempt to understand the candidate's perspective on the business side of things that the role would entail. They may ask the candidates how they would serve the long term business interests of the company. They may also ask how deployment of data science knowledge and expertise might help the company tide over its current and foreseeable business challenges.
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Overview
After the Phone Screen, the candidate is called for the Onsite Round. On average, 5-6 rounds of interviews are conducted as part of the Onsite round. Each of these rounds lasts for anywhere between 45 to 60 minutes.
The onsite round focuses on a vast array of technical topics that includes
- Fundamentals of data science
- Ability to program and work with data
- ML Libraries (for engineering DS roles)
- A business case study on analytics or model development
Let's deep dive into what is asked from each of these topics.
- Fundamentals of Data Science
Here the candidate would most likely be asked questions on various machine learning algorithms such as linear and logistic regression, decision tree, random forest, deep learning models, artificial intelligence, and neural networks.
A few questions involving the day-to-day application of Data Science concepts may also be asked.
Q - How would you find feature importance in a neural network?
Q - Explain the Naive Bayes algorithm.
- Ability to program and work with data
In this part, your programming and database management skills are going to be tested. The focus will be on whiteboarding coding solutions in languages such as Python and Java.
For database management questions, brush up on your MySQL.
Q -Write a Python code to calculate the standard deviation of elements in a list.
- Machine Learning Libraries
If you are appearing for an Engineering Data Scientist role, you will be asked questions on various machine learning libraries and their applications.
Q - Give us an example of the application of TensorFlow in a production system.
- Business Case Study on Analytics or Model Development
Here, the candidate will be tested on his ability to apply his technical expertise in the product/ model development stages. The interviewers will check if the candidate can select the right metrics, understand the technical details on hypothesis testing, and how he deals with pitfalls in experimentation.
Q -Tell us how you would go about building a model to predict Uber ETAs after a rider requests a ride.
Tips
- Practice your concepts well, with a special focus on the Product sense part for the business case study.
- Let the interviewer know your approach to the problem and not just the solution.
- Do not hesitate to ask clarifying questions to the interviewer.
Interview Questions
Most asked interview questions in the Onsite Round
- Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?
- Mean and Gaussian mixture model: what is the difference between K-means and EM?
- Simulate a bivariate normal
- Derive variance of a distribution
- How do you build estimators for medians?
- If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?
PayPal Data Scientist interview might be a tough nut to crack, but with a planned preparation strategy and sufficient practice, we are sure you will ace the interview.