When the recruiter told me that the hiring manager might see me as a better fit for a lower position, I couldn't help but be suspicious. However, I went ahead with the interview, hoping to see things for myself. I was taken aback when I asked the hiring manager what kind of optimization models they used, and his answer was "AutoML." I followed up by asking if the team developed their own machine-learning models in Python, the manager's response was defensive, pointing to data limitations and difficulties in extracting information from multiple sources. Which I could empathize with since I ran into the same issues before. However, that issue can easily be ameliorated by working with a data engineer and some feature engineering. What struck me as odd was the manager's earlier claim of having access to millions of records and countless fields spanning over the years. With so much data, I wondered, shouldn't the team be capable of experimenting with, building, and validating their own models? And not relying on AutoML. Throughout our conversation, I couldn't shake the feeling that the team members were more like statisticians trying to pass as data scientists. The language used during the interview seemed to confirm my suspicion. The hiring manager spoke of dependent/independent variables, while I referred to them as target/features. Though the terms mean the same thing, consistency in the language is crucial since this is a Data Scientist role, not a Statistician role. Overall, my experience was no exception to the pervasive issue of highly technical jobs being filtered by non-technical managers misconstruing what the role of a Data Scientist actually entails.