Real, anonymous reports from people who interviewed for Data Scientist at PayPal, newest first and distilled into what to expect across the loop.
After an initial recruiter screen, I went through four structured rounds. Round 1 mixed practical questions across SQL and Python with some DS/ML theory. Round 2 shifted into a mini risk-style case, then paired it with more SQL and Python questions. Round 3 brought back a mini risk case and also leaned into questions tied to my past experience. Round 4 moved toward business strategy, again using a risk framing, and it also pulled from my background.
After an online test and being shortlisted, I had a one-hour interview that felt like a deep dive into advanced ML topics. The questions covered AI and ML broadly, but they also got specific around RAG, statistics and probability, and even more applied GenAI themes like genetic algorithms. The interviewer’s framing made it clear they were looking for someone who’d been consistently preparing for quantitative, data-science-heavy roles.
The interview process typically includes an initial recruiter screen followed by multiple structured rounds, often focusing on technical skills, case studies, and business strategy, with an emphasis on risk management throughout.
Candidates should expect multiple technical assessments, particularly focused on SQL and Python, with questions ranging from basic syntax to complex query logic and data manipulation tasks.
Interviews often delve into advanced machine learning topics, requiring candidates to connect theoretical knowledge with practical applications, including specific algorithms and statistical concepts.
Expect discussions around past experiences and behavioral questions that assess motivation and alignment with the company's values, often integrated with technical evaluations.
Some candidates may complete home assignments or case studies that require practical application of skills, followed by discussions of their results in subsequent interviews.
While the interviews are structured, candidates often report a lack of timely feedback or closure after the process, which can leave them uncertain about their performance and the decision-making criteria.