I've been working with data science teams for over a decade, and the gap between what we teach in bootcamps and what actually happens on the job is staggering. Most graduates can build a model, but almost none know how to frame a business problem or question the data's validity. Is the real bottleneck in data science not technical skills but the ability to think critically about the context and limitations of every analysis? I've seen projects fail not because the algorithm was wrong, but because the question being asked was meaningless in the first place. Would companies benefit more from data scientists who deeply understand the domain rather than those who can tune hyperparameters?