Navigating Data Science Talent
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Over several days last week, I reacquainted myself with the complexities of the London Underground system.
It’s only been a month since I was last down on business and I’ve already forgotten the easiest routes and am completely reliant on a London Tube Map to keep me on track.
To a novice, a “tube noob” like myself, the underground is initially viewed as a myriad of coloured lines spiralling out of control across London. In comparison to where I live, in Glasgow, the tube is one big circle that goes around and around, which must be a novel amusement to the visiting hard-core London commuter.
The reason I’m writing is that I had the pleasure of meeting three uniquely different clients in terms of industry and stage of growth, each with their own Data Talent challenges.
One client was a leading entertainment brand, the other a well-known global consultancy and the last was a specialist Analytics and Data Science start-up consultancy… All conveniently located at each side of London for me to locate via an underground maze of tunnels.
What I realised with all three clients is that, after our initial discussion, it was apparent they are all searching for a complex and uniquely talented Data Science candidate to help achieve their specific business goals. Every client had a different way of explaining their needs and requirements due to the way their business was structured.
However, every conversation more or less went like this in terms of the initial job brief.
“We are looking for a Data Scientist, who is educated at the right level, has commercial experience, is able to utilise tools such as SQL, R, Python proficiently and who can explain complex Data Science methodologies in a digestible manner to senior stakeholders or non-technical audiences”.
You would think in 2019 this would be an easy task? Not entirely.
It became apparent after a few probing questions that they each had a preferred specialism or path they wanted their candidates to come from. The entertainment brand was Customer Insight focused and works heavily within a Machine Learning context. The global consultancy purely wanted NLP and Cognitive Neuroscience specialists, and finally, the start-up consultancy is looking for Data Science consultants who can create immediate value from stakeholder interactions and generate understanding through communication rather than pure numbers.
So, just like the London Underground Map, every role has branched off into different paths or sub-sets of what a Data Scientist is and what they can or want to do with their career.
My observations of the Data Science field over the last few years would wholeheartedly confirm my clients’ requirements.
Specialisms are being established and recognised within the Data Science sector, leading to more choice in terms of career paths for Data Scientists and providing more clarity for the hiring organisations who can now find it easier to refine the specific skillsets for their unique business needs.
This, in turn, makes it easier for us to properly identify the right candidates for the right roles at the right time.
So…if you are going around in one big circle “Glasgow Underground Circle” with your recruitment process when it comes to hiring Data Scientists, think like the London Underground. Consider branching out and be specific with what you require. Don’t just call a role a “Data Scientist” if NLP is the main skill you require. The more you focus on what you need the better chance you will find the right person and that starts with the job description and briefing conversation.
Author: Joshua Smith