There is a conversation happening in every tech company right now. A data scientist presents a model. It has 94% accuracy. The AUC-ROC is excellent. The confusion matrix looks clean. Everyone is impressed. Then someone asks: "How do we use this in our product?" Silence. The model lives in a Jupyter notebook. It has never seen real user input. It has no API. It cannot be called from a frontend. It cannot be deployed. It exists purely as a demonstration of what could be — not what is. This is the gap that costs companies millions of dollars in delayed products and wasted engineering time. And it is the gap that makes full-stack ML engineers the most valuable technical hire in the market right now. The Myth of the Pure Data Scientist The traditional data science role was defined by a clear boundary. Data scientists build models. Software engineers deploy them. These are separate disciplines requiring separate people. This made sense in 2015. It makes much less sense in 2026. The tools have changed.…