Process: Three rounds — recruiter screening, hiring manager interview, and a final senior leadership panel. The process was straightforward and moved at a reasonable pace. What went well: The interviews themselves were surprisingly pleasant. Both the hiring manager and senior leadership rounds felt more like genuine discussions about data science topics than formal interrogations. The interviewers were knowledgeable, engaged, and easy to talk to. What was confusing: The job description and the actual interview discussions didn't align well. The JD places heavy emphasis on causal inference, experimentation, and decision science — to the point where I spent significant time preparing for those topics. However, neither interview touched on causal ML at all. Instead, LLMs and GenAI — which appear as a relatively small part of the JD — took up roughly a third of the first interview and a fifth of the second. If you're preparing for this role, know that the interview may test you on different areas than what the JD suggests. Advice for candidates: Apply if you're interested, but go in with open eyes. Prepare broadly across data science topics rather than anchoring too heavily on the JD. There also seemed to be factors behind the scenes that I couldn't fully read — the process felt somewhat opaque at times, which contributed to uncertainty as a candidate. Overall: A professional and pleasant interview experience, but the disconnect between the job description and interview content made it hard to prepare effectively and left some unanswered questions about the role's true focus.

The interview process was structured and professional. It began with an initial recruiter screen followed by a technical phone interview. After that, I was invited to a virtual on-site that included a case study, technical questions on machine learning and Python, and a behavioral interview with the hiring manager. The process took about three weeks overall. The interviewers were courteous, and the questions were relevant to the role.