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Feature

The Myth of the Mythical Unicorn

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Many have claimed recently that multifaceted data scientists are mythical beings, as impossible to find as unicorns. This itself is a myth, and a dangerous one at that.

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Comments (2)
Thanks for this perspective Shiomo. Those skilled individuals are out there if you look hard enough, and certainly there are plenty of good professionals with data management and analytic modelling skills.

What I think is missing is people with the capability to develop *narrative* from data - there's a world of difference between preparing the facts, and interpreting meaning and action that should arise. (And analytic thinking is pretty much absent within the Executive tier, in my experience).

There also needs to be co-existence, rather than conflict, between the Data Scientist/Analyst and the Data Manager/Steward, as I noted in my blog earlier this year: http://informationaction.blogspot.com.au/2014/02/hunting-for-unicorns.html

Posted by Alan D | Thursday, May 22 2014 at 8:05AM ET
I'm afraid you do not understand the concept of the "unicorn" with respect to data science hires. It is not that data scientists are mythical as unicorns. Is is that there are different types of data scientists; model designers and algorithm architects, engineers (coders, hardware architects, sys admins), visualization experts, etc. The debate about unicorns centers around trying to find a single data scientist with all these skill sets. Instead, it is much wiser to hire a team of data scientists, each contributing specific experience. There are many companies holding out for the unicorn. One company I know of has had a unicorn ad running for well over a year. They've interviewed 100s of data scientists without a hire. This is because their unicorn candidate doesn't exist. Some of these companies are just trying to hire on-the-cheap, HR's reasoning is if they can hire a single unicorn for $150K/yr, why hire 3 at the same pay? Other companies, just don't know enough about hiring for this position, so they just copy/paste areas of experience from across the data science spectrum, thinking that one person can fit the bill. No bloody likely!
Posted by Daniel G | Thursday, May 22 2014 at 6:36PM ET
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