This is an updated version of a post originally published on Medium on Jan. 28, 2016. I may have more to say about this sort of thing in the near future.
There are different types of data scientists, with different backgrounds and career paths. With Sean Murphy and Marck Vaisman, I wrote an article about this for O’Reilly a few years back, based on survey research we’d done. Download a copy, if you haven’t read it. This idea is now pretty well established, but I want to talk about a related issue, which is that the type of work that Data Science teams do varies a lot, and that managing those types of work can be an interesting challenge.
As Josh Wills said, data scientists aren’t software developers, but they sometimes do that sort of work, and they aren’t statisticians, but they sometimes do that sort of work too. At EAB, where I lead a Data Science team of people with very diverse backgrounds and skill sets, this issue leads to a lot of complexity and experimentation as we (and the upper management I report to) try to ensure that everyone is working on the right tasks, at the right time, efficiently.
In this post, I’d like to share some thoughts about how we currently think about and manage different types of Data Science work. I also wrote a little Shiny web tool to help us manage our time, and I’ll show that off as well.