The Data Scientists need a large set of skills, including business know-how, modelling and mathematics, plus programming. They are as hard to find as unicorns, or superheroes. I know this talent shortage first hand. Is the solution to create more unicorns, or can we devise better solutions?
In my last role as a managing consultant in the Operations Research and Analytics team of a large global consultancy, I also ran recruitment. Having spoken to or met 150-200 of such candidates personally, and my recruitment team saw multiples of this number, I can tell you not many of those candidates made the cut. That’s because they didn’t have all of the skills we were looking for. Andwe were only looking for the first 2.5 of the 5 core skill-sets of a data scientist below. “Good luck” is what people offer to this talent-search problem, but I think we can get around the unicorns.
Expanding on the data science venn diagram, I think the following 5 skills deserve closer attention, separately*.
Furthermore, each of the above have subfields and specialties, because they are complicated in their own right. It is not possible to be very good at so many things, not at scale anyway or to be above mediocracy at best. How many sportsman/woman excel at more than one sport, for example?
The thing is, these people all exist, have existed, and will exist. They are just separate individuals. They have labels likebusiness analytics consultants, statisticians and modellers (operations researchers included), data visualisation experts, DBAs and software engineers. Yes, they are also talents in need, but they are not unicorns. If we need data scientists in troves, we need a team, not just a few geniuses.
People should diversify a bit, for instance a modeller should be able to code, but ultimately they need to specialise in something they are good at. A modeller must be able to prototype on his/her own, which requires coding skills, but s/he shouldn’t be expected to produce production-ready code for large scale applications. Similarly, asking a good modeller to do database administration and ETL tasks is a waste of talent, Hadoop or not.
Specialisation is the reason for humanity’s proliferation. Therefore, I’d say it’s not the people we need to change, but the system that we need to setup to allow such specialised workforce to team up together. It’s lazy for the analytics field to put up its feet and just summon one person to provide it all.
As a starter-for-ten, I think the future of our field could be modelled after the traditional IT project group make-up:
There will be complications to address. To name a few…topics for another post:
Where do you think the analytics field is heading to?
By: Dawen, Operations Research / Management Science Consulting, thinkor.org
Originally published at thinkor.org
Disclaimer:
My views are definitely biased by my background: I am a manager in business analytics consulting, trained in Operations Research and Computer Science.
* My expansion on the data science venn diagram’s 3 skills are based on various articles, such as O’Reilly guide, Intro to DS skills, and job requirements on numerous current data science job posts.
