Why So Many ‘Fake’ Data Scientists?

Have you noticed how many people are suddenly calling themselves data scientists? Your neighbour, that gal you met at a cocktail party — even your accountant has had his business cards changed!

There are so many people out there that suddenly call themselves ‘data scientists’ because it is the latest fad. The Harvard Business Review even called it the sexiest job of the 21st century! But in fact, many calling themselves data scientists are lacking the full skill set I would expect were I in charge of hiring a data scientist. 

What I see is many business analysts that haven’t even got any understanding of big data technology or programming languages call themselves data scientists. Then there are programmers from the IT function who understand programming but lack the business skills, analytics skills or creativity needed to be a true data scientist.

Part of the problem here is simple supply and demand economics: There simply aren’t enough true data scientists out there to fill the need, and so less qualified (or not qualified at all!) candidates make it into the ranks.

Second is that the role of a data scientist is often ill-defined within the field and even within a single company.  People throw the term around to mean everything from a data engineer (the person responsible for creating the software “plumbing” that collects and stores the data) to statisticians who merely crunch the numbers.

A true data scientist is so much more. In my experience, a data scientist is:

• multidisciplinary. I have seen many companies try to narrow their recruiting by searching for only candidates who have a Phd in mathematics, but in truth, a good data scientist could come from a variety of backgrounds — and may not necessarily have an advanced degree in any of them.
• business savvy.  If a candidate does not have much business experience, the company must compensate by pairing him or her with someone who does.
• analytical. A good data scientist must be naturally analytical and have a strong ability to spot patterns.
• good at visual communications. Anyone can make a chart or graph; it takes someone who understands visual communications to create a representation of data that tells the story the audience needs to hear.
• versed in computer science. Professionals who are familiar with Hadoop, Java, Python, etc. are in high demand. If your candidate is not expert in these tools, he or she should be paired with a data engineer who is.
• creative. Creativity is vital for a data scientist, who needs to be able to look beyond a particular set of numbers, beyond even the company’s data sets to discover answers to questions — and perhaps even pose new questions.
• able to add significant value to data. If someone only presents the data, he or she is a statistician, not a data scientist. Data scientists offer great additional value over data through insights and analysis.
• a storyteller. In the end, data is useless without context. It is the data scientist’s job to provide that context, to tell a story with the data that provides value to the company.

If you can find a candidate with all of these traits — or most of them with the ability and desire to grow — then you’ve found someone who can deliver incredible value to your company, your systems, and your field.

But skimp on any of these traits, and you run the risk of hiring an imposter, someone just hoping to ride the data sciences bubble until it bursts.

What would you add to this list? I’d love to hear your thoughts in the comments below.

Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management, technology and the mega-trend that is Big Data. If you would like to read my regular posts then please click 'Follow' and feel free to also connect via Twitter, Facebook and The Advanced Performance Institute.

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• Smart Big Data: The All-Important 90/10 Rule
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• From Farming To Big Data: The Amazing Story of John Deere

About : Bernard Marr is a globally recognized expert in big data, analytics and enterprise performance. He helps companies improve decision-making and performance using data. His new book is Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance. You can read a free sample chapter here.