[jupiter90000]

Part of the issue as I see it (for me, unrelated to the article), is that companies are willing to use the data scientist term for positions that need none of the rigor you mention. However, the people were hired and are now called a data scientist.

The same type of thing seems to happen in other fields, too. Software engineers who don't engineer, data scientists who don't 'science', project managers who don't manage. Are they top in their field? No, they somehow have a job with the title though and so far have managed to not become unemployable. Do they care if they are rigorous in what their title is expected to be by top practitioners? Probably not, they get paid still and have the title, and can probably get hired at the next similar place.

Kind of sad that these positions may 'cheapen' the title, so what can be done about that? Not much I guess, since companies can use position titles as they'd like it seems...

[avn2109]

In my (admittedly short) experience as a data scientist, "solving the wrong problem"/"working on irrelevant things" and "inadequately cleaned/prepped training data" are vastly, overwhelmingly more common failure modes than "building the right thing with good data inputs but misunderstanding the algos." Probably more common by an order of magnitude or two.

Then again, maybe I'm just working at companies with problems that are amenable to easily-understood algos but have plenty of data-and-product-themed problems.

[jupiter90000]

Great points about issues faced before dealing with models/algos directly. Understanding something about the models/algos can help guide data prep too.