(not that long ago) when being a data scientist meant living in a notebook, tweaking hyperparameters as if your life depended on it, and in a lot of cases, the whole project did, indeed, depend on it. Do you remember those overnight grid searches? Or building feature engineering pipelines that felt more like art than science? And the satisfaction of squeezing out an extra 0.7% accuracy from an XGBoost model? Back in 2019, that was the job of a data scientist! Which made sense. If you wanted a strong model, you had to build it yourself or work hard to get it right. The real value came from how well you could tune, optimize, and understand the data. Now, ‘state-of-the-art’ is just an API call away. Need a top language model? Done. Need embeddings or multimodal reasoning? Also done. The hardest parts of modeling are now handled by scalable endpoints, far beyond what most teams could build themselves. The question now is, if the model is already there, where did the work go?…