Data Science vacancies are widely renowned as one of the toughest roles to recruit, indeed the shortage of Data Scientists within the market is top of many discussions we have when recruiting for this role. Our recent Salary Survey demonstrates that there are far too few candidates to meet the rising demand. However, the first hurdle we face here at Xcede when speaking to a company looking to hire in this market is defining what the Client means by a Data Scientist?
Let’s go back to the beginning
In 2008, DJ Patil and Jeff Hammerbacher co-coined the term “Data Scientist” to define their jobs at LinkedIn and Facebook respectively. A few years ago when the likes of Facebook and Google started searching for these “unicorns” there was a specific criteria they were searching for. At Xcede we refer to Anjul Bhambhri's (VP of Big Data products at IBM) definition:
“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organisation”.
The job title itself seems to have lost this element over the past few years. Granted, the bulk of this is due to buzzwords and the hype around Data Science – after all, who wouldn’t want the title of “Sexiest Job of the 21st Century”. The question is, is this such a bad thing?
Sure, there are many companies who are posting job vacancies in Data Science that include descriptions for “Data Scientists” which aren’t, and there are many people calling themselves “Data Scientists” who aren’t. But the very vague overall goal is the same – we have a whole load of data and need someone to help us come up with some insight from it to move our business forward.
What are the Biggest Changes?
The biggest developments that we have seen over the past two years is that companies are no longer searching for an individual “unicorn” who can do this but are looking to build teams with complementing skillsets… and rightly so! The Data Scientist title has been broken down (or gone back to, depending which way you look at it) other titles such as Machine Learning Specialist, Statisticians, Python-istas, and the new buzz-role “Data Artists”.
However, it is argued that the Data Scientist bubble could burst as Jean-Pierre Dijcks states “the future of Data Science is smarter tools, not smarter humans”. The point being, as an increasing number of organisations jump on the Data Scientist bandwagon, they have now become wise to the need to hire a team of specialists. This is why such articles as “7Questions to Ask before Hiring a Data Scientist" are surfacing and providing a great insight into helping companies explore the real reasons they need a Data Scientist.
What does this all mean?
The job market is changing…yes, more companies are hiring Data Scientists, but they also want to get their money’s worth from them. At Xcede we still receive a “shopping list” of desired skills when receiving job descriptions. In a lot of cases those companies need educating on what Data Science can do for them and what their realistic expectations should be.
So what is the future for the Data Scientist job title? We see there being two parts to its recent evolution. Firstly, despite many claims that the Data Scientist job title will disappear, we believe that the role will always exist in one form or another with the possibility of the number of “true” Data Scientist positions and professionals stagnating. Secondly, and perhaps the more noticeable trend of the past 12 months, teams of more specific skillsets will be built out around these Data Scientists, or Data Science teams will be built with complimentary skillsets.
No matter what happens, the Data Science job market is easily one of the most interesting and debated… and long may that continue!