[discmonkey]

I have to agree. Especially given the very real possibility that your ML project won't be cutting edge research grade. At that point someone who doesn't have bias and is willing to search for a reasonable looking approximation to the problem and try a canned solution may actually be an optimal candidate.

[0cf8612b2e1e]

Considering the number of problems that could be plugged into a random forest with good results, data proficiency seems more important than strong ML experience.

[uoaei]

Depends heavily on the application once you get to more specialized domains.

I wish there was an easier way to label roles differently based on when you just need to throw X or Y model at some chunk of data and when more specialized modeling is required. Previously it was roughly delineated by "data science" vs "ML" roles but the recent AI thing has really messed with this.