[monadmancer]

I think the obvious "gotchas" are problem definition (Am I formulating the problem in a way that will allow me to create value? a concrete example: am I modeling churn correctly?), overfitting, target leaks, and model trouble shooting / improvement (i.e. the model is doing OK, can it do better? How much better? How do we get there? Remembering that small performance gains can mean big $ at scale). On the reporting side, how confident that what I'm reporting is real? This is where the "science" training is helpful. Programming experience is relevant in the sense that implementation is important, i.e. it's far too easy to introduce critical target leak bugs when engineering features.

Of course we can abstract the root argument; for a given job, among those qualified to fill that job, there exists at least one person who has auto-learned the skills required to perform the job. This is probably true.