Unpopular opinion: Most “data science” jobs are just SQL monkeys wrapped in a fancier job title.

Let’s be real — the majority of “data science” work out there isn’t model building, algorithm design, or any of the sexy stuff from your machine learning course.

It’s dashboards, ad-hoc SQL queries, A/B test analyses, and reporting that somehow takes 80 hours because three tables are undocumented and the schema was designed by a caffeinated squirrel.

We’re calling it “data science” because it pays better than “BI analyst” or “data analyst,” but if we stripped away the buzzwords, most of us are doing:

Data wrangling Answering business questions Cleaning up after inconsistent tracking Writing the same LEFT JOIN 12 different ways

…and maybe, maybe training a basic model once a quarter — if it clears stakeholder buy-in and the data isn’t garbage.

I’m not saying this work isn’t important. It is. But let’s not pretend that 90% of data scientists are doing cutting-edge ML research.

They’re just the new Excel wizards, except now it’s in a Jupyter notebook and we added “Bayesian” to the slide deck.

Change my mind.