When I applied for this data scientist job, the interview process kicked off with a quick recruiter screen where I walked through my background and tried to sound naturally enthusiastic while also praying they wouldn’t ask me to define MLOps in a sentence. Then I moved on to a technical phone round, which turned into an unexpectedly deep dive into pandas operations and left me second-guessing everything I thought I knew about DataFrames. After that came the “simple” take-home assignment that somehow ate my entire weekend and required me to dust off parts of linear algebra I hadn’t touched in years. Finally, the onsite loop involved a whiteboard session where I derived logistic regression under the mildly judgmental stare of three engineers, a product case where I had to improvise strong opinions about churn, and a behavioral round where I repeated “cross-functional collaboration” so many times that I briefly wondered if I was still speaking English.