[volker48]

The surface area of "gotchas" in data science is much larger than software engineering because you have all the "gotchas" from programming/software engineering AND all the new "gotchas" from data science.

For data science the big one is over fitting, which everyone talks about, but can happen in really insidious ways in production. You have to be very disciplined and careful with the data to prevent over fitting.

Another big one is productionizing data science, which in my opinion most data scientists don't have a ton of experience with.

The actual training of the models part of data science isn't that hard, its actually making it work with the crappy data that exists in the real world and putting it into production that are the really hard parts.