My take on this is what's needed to succeed in DS/ML in a commercial setting is a) domain knowledge b) the diligence to acquire, clean, and generally pre-proces

My take on this is what's needed to succeed in DS/ML in a commercial setting is a) domain knowledge b) the diligence to acquire, clean, and generally pre-process the data and c) some fairly basic knowledge of ML. Someone with amazing ML skills but no knowledge of the problem domain will fail. Someone without domain knowledge will be unable to assemble and curate the data they need and will be unable to make any sense of the output. That is the Achilles heel of ML rockstars who think it's just a matter of getting enough data into their training set and the NN will do the rest.