Does the Ideal Data Scientist exist and where do they come from?

Posting date:10 Sep 2019

We recently produced a white paper, focused on the human face of Data Science. We explored the evolution of the Data professional, the relevance of the phrase ‘Scientist’ and questioned whether we should scrap the term altogether, replacing it with a list of more specific job titles.

We also battled with the idea of the Ideal Data Scientist and questioned what they looked like. Were they educated? Personable? Analytical? Or, perhaps creative? 

To reach some sort of conclusion, we decided to ask the question to our network; made of more than 2,000 Senior Data Science Professionals and the organisations that home them. 

They were equally torn on the matter of the ‘ideal education’ as 50% stated that the ideal professional had a degree, PhD and further post-doctorate while 46% believe they should be non-degree educated and just 4% said a degree with no post-degree qualification would be ideal.

For me, this debate is fascinating. I completely agree with the principle that Data Professionals may or may not require an education but there are many different considerations that need to be taken into account first. This includes their primary objective as a new hire, the working environment and the type of industry.

If you are working for a media company for example, you need to be more creative to fit both the brand and its internal messaging while if you work in the Financial Services sector – you need to be more analytical.

Don’t get me wrong, a Data Scientist always needs to be analytical. They need to be able to spot the trends and then come up with a solution to any problem that arises as a result, however, if we think to the profile of a Data Engineer, they need to be more hands on and technical and a PhD would almost be irrelevant. 

It is vital when considering a new hire that you think about the role they play and not just the title we bestow upon them as the term ‘Data Scientist’ spans across so many different disciplines. Equally, there isn’t a one-size-fits-all approach. 

Every company should look at their own requirements before thinking of a key profile of a person they need to solve the problems. You need to think firstly, what are your key problems and then secondly, how are you going to solve the problem or who is going to help you?

Do you think the ‘Ideal Data Scientist’ exists? Or, do we need to think about the professionals we bring in to carry our businesses forward on an ad-hoc basis? Join the conversation and in the meantime, download a copy of our white paper ‘The Human Face of Data Science’ below.

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