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This is interesting because one of the criticisms towards data science have been that it's a term so broad that two people can have the title data scientist and

This is interesting because one of the criticisms towards data science have been that it's a term so broad that two people can have the title data scientist and do completely different jobs. Hence the differentiation over time into research scientists, ML engineers etc. Personally I might prefer the generalist hat but I see things moving in the opposite direction. There is also the problem of hiring - if 1-10% of the job requires a lot of statistical theory/research and the rest doesn't, do you hire PhDs and ask them to mostly do the engineering tasks that they aren't necessarily prepared to do? What about the people who are perfectly capable of doing 99% of the job and picking up the extra 1% of theory - do you exclude them because they don't have the academic credentials?

lumost 6y ago
I would take the approach that there is a spectrum of knowledge and hire the quote "T" shaped individual. Strong in at least one area, but broad enough to contribute anywhere. People change faster than orgs/titles, it would be better to keep this part flexible to maximize their growth. This can be further extended to the whole engineering culture. It's not uncommon that a frontend engineer shares a title with a backend software engineer - these skills are different enough that the individuals are no longer fungible for the most part. Why not give DS's engineering titles and treat it as a management objective to ensure you have the right people on any given project? If scientist is the preferred title, why not give anyone the science title?