More often than data scientists would like to admit, machine learning models produce results that are obvious and intuitive. For instance, we once conducted an elaborate analysis of physicians, prescribing behavior to find out the strongest predictor of how many prescriptions a physician would write in the next quarter. We used a broad set of input variables such as the physicians locations, their specialties, hospital affiliations, prescribing history, and other data. In the end, the best performing model produced a result that we all knew very well. The strongest predictor of how many prescriptions a physician would write in the next quarter was the number of prescriptions the physician had written in the previous quarter! To filter out the truly meaningful variables and build a more robust model, we eventually had to engage someone who had extensive experience of working in the pharma industry. Machine learning models work best when produced in a hybrid approach—one that combines domain expertise along with the sophistication of models developed.