Data scientists type A & B? Nonsense.

By Thilo Stadelmann (ZHAW)

Reposted from https://dublin.zhaw.ch/~stdm/?p=350#more-350

I recently came about the notion of “type A” and “type B” data scientists. While the “type A” is basically a trained statistician that has broadened his field towards modern use cases (“data science for people”), the same is true for “type B” (B for “build”, “data science for software”) that has his roots in programming and contributes stronger to code and systems in the backend.

Frankly, I haven’t come about a practically more useless distinction since the inception of the term “data science”. Data science is the name for a new discipline that is in itself interdisciplinary [see e.g. here – but beware of German text]. The whole point of interdisciplinarity, and by extension of data science, is for proponent to think outside the box of his or her original discipline (which might be be statistics, computer science, physics, economics or something completely different), and acquire skills in the neighboring disciplines in order to tackle problems outside of intellectual silos. Encouraging practitioners to stay in their silos, as this A/B typology suggests, is counterproductive at best, fatal at worst.

If you want to counter the infamous “unicorn” description of a data scientist who is an expert in each and everything, take the distinction my colleagues from the University of Sheffield introduced to me: While the “type I” data scientist is himself a manager, bothered with hiring and leading data practitioners and having a more high level view of data sciences’ potentials and workings, a “type II” data scientist knows how to “do the stuff” technically. This opens up the way to combined curricula for manager-type people and technically-oriented people.

But to me, the attempt to distinguish – isolate – sub-types of “type II” data scientists, is just re-labeling of good but a little bit old-fashioned names we once where used to (statistician, business analyst, BI specialist, data miner, database engineer, software engineer, etc.). There’s nothing wrong with these titles if they fit the job. To re-label them only because “data scientist type A” is more fashionable, might be good for the individual’s self esteem; it is dangerous nonsense for the industry and (scientific) discipline as a whole.

[personally signed contributions reflect the opinions of their author and not necessarily those of datalab]

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