The “Data Scientist” label served its purpose; it allowed us to signal a transition happening in our profession from using only applied mathematical statistical methods to something else, which now also involves the use of a subset of software engineering practices. This transition was mentioned back in 2010 by Deborah Nolan (https://www.stat.berkeley.edu/~statcur/Preprints/ComputingCurric3.pdf), and this transition might now be complete. Version control systems, document generation from annotated source code (or even full reports generation à la rmarkdown), containers and build automation tools have now entered the toolbox of the run-of-the-mill statistician. Maybe not all of these tools, of course, it largely depends on what it is exactly you do, but certainly some of these. Same goes for software engineering practices. I have had the opportunity to work with some old-school statisticians (and still do), and the difference is clear; just like old school users of WYSIWYG editors like Word don’t use its “newest” features such as “Track changes” (and thus keep writing their text in different colors to signal which paragraphs are new), or the concept of versions of a document synced on Sharepoint (and thus keep multiple versions of the same document with different names) old school statisticians have not included the tools I mentioned before in their toolbox.

Now don’t get me wrong here; that is absolutely ok. We need and respect old school statisticians because they’ve been in the business of getting insights from data for longer than I’ve been alive. This blog post is not a jab at them because they don’t know how to use git (if you interpret it like that, that’s on you). Old school statisticians now have very senior positions and for many of them, their job does not involve getting their hands dirty on data anymore; most of them are now more like managers or mentors, and share their deep knowledge with their more junior team members. (Obviously there’s exceptions, when I say all old school statisticians do this or that, I don’t mean all of them, but most of them. Of course, I don’t have any evidence to back that up).

What this blog post is about is the label “Data Scientist” that gets used by these more junior team members and by companies that want to hire talented and motivated young people. This label, and the purported difference between a “Data Scientist” and “statistician” does not make any sense in 2020 anymore. (I know I’m beating a dead horse here, but this is my blog. I’ll blog about dead horses as much as I want thank you very much.)

Firstly, this label has always been confusing. “Data Scientist”… what does it even mean? The fact it took so long to find a definition, and that almost everyone working in the profession has a different one speaks volumes. Also, don’t all scientists use data? Data from experiments, from observational studies, from surveys, from the literature?

Secondly, I don’t believe that you can get a degree in statistics today without any exposition whatsoever to at least some of the tools I mentioned before. I really doubt that there’s people out there getting Master’s degrees in statistics without having ever touched these tools, or the unix command line. The degrees they’re going for might not focus a lot on these tools, true, but they certainly touch upon them. And of course, once they join a team at their first job, they’ll get more exposed to these tools and incorporate them in their day to day work. So, they’re not statisticians anymore? Their degree magically transformed into a data science degree?

But what about data science degrees? Are the students graduating with these degrees statisticians? I’d argue that yes, they are indeed statisticians; it’s just that they took a statistics degree that might have focused more than usual on these “new” practices/tools, and changed its name to “Data Science degree” for marketing purposes.

Anyways, the label “Data Scientist” is now completely defunct; as I mentioned in the very beginning, it served us well to signal that a transition was happening in the profession. I believe that this transition is now complete, or should be nearing its final stages. Also, this transition was not only about the tools used, but also about the deliverables. Statisticians now don’t only deliver tables, graphs and studies but more and more of them deliver products. This product can be a package implementing a bleeding edge statistical method for the profession as a whole, or it can be part of a piece of software that needs it to run (like your smartphone keyboard using a statistical model for word predictions). See this paper for an interesting exposition about how curricula and deliverables have evolved in the past two decades.

Currently, this label gets used by people that try to get insights from data. But we already have a word for them; statisticians. It’s just that the tools of the statistician have evolved over the past decade or so. Actually, I would perhaps even make another distinction; we should reserve the label of “statistician” to people that do statistics without ever touching any data. The other statisticians, the ones that get dirty wrestling in the mud with the data (they’re the pigs that like it from that famous quote) should be called “data janitors”. I’m not even joking; not only does that term already exist and gets used, I think it suits what we do perfectly. What do janitors do? They clean stuff and put things in order. We clean data and put it in order; meaning creating summary tables, visualizations, interactive applications, and models. Oh, and we do so (preferably) in a reproducible way.

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