while I agree with your second thought, I don't agree with your first. Kaggle is like a sport and therefore can't and shouldn't be comparable to real-life data-

while I agree with your second thought, I don't agree with your first. Kaggle is like a sport and therefore can't and shouldn't be comparable to real-life data-science (I mean, who want's to compete in tasks modeled after real life? :D It's often A LOT messier in rl!). BUT I think kaggle still trains you in the right things. In my experience, the winners were almost always more creative and didn't win just through massive hyperparemeter grid-search. I also think that a lot of the usual non-ds struggles are just not as challenging. If I would be in the position to hire a DS and theoretical skills would not be that important (maybe there's already somebody with strong theoretical skills in team), i would just hire somebody based on his kaggle profile. It will probably take some time till he adjusts to real-world DS, but I don't think it will be that of a challenge.