Working data scientist here. As many have said, this is, effectively a Kaggle challenge. I mean honestly at this point, I don't care, at all, how well someone can predict anything to be a data scientist - there is very little correlation between that and how good of a data scientist they are.
Tools to hire data scientists and going to continually fail until they realize that the interesting, hard part of being a data scientist is closer akin to a business lead (which can't really be tested in 60 minutes).
Concrete feedback:
- You ask for writing and descriptions on why a model was chosen, why features matters - are you grading this automatically? That would be a feat.
- The task is waaay to easy (even if you do believe there is a market for identifying people who can predict well).
Tools to hire data scientists and going to continually fail until they realize that the interesting, hard part of being a data scientist is closer akin to a business lead (which can't really be tested in 60 minutes).
Concrete feedback:
- You ask for writing and descriptions on why a model was chosen, why features matters - are you grading this automatically? That would be a feat.
- The task is waaay to easy (even if you do believe there is a market for identifying people who can predict well).
- Python is overly limited. Why not SQL or R?