Your company needs a data scientist… doesn’t it? It very well may not, but you need to know either way. Read on to determine whether or not your company could benefit from the skills of an on-board data scientist.
It’s been called the “sexiest job of the 21st century”, and is attracting a flood of new entrants.
Recent reports indicate that there are 11,400 data scientists who have held 60,200 data-related roles. And the overall count has grown 200% over the last 4 years, across Internet, Education, Financial Services, and Marketing industries.
And yet amidst a field growing so fast, you can observe a bit of confused exuberance. It’s not uncommon for a company to hire a data scientist just after product launch, or after Series A. To some, data science has become the magic bullet for achieving scale or their next inflection point.
But what does a data scientist do? And does your company actually need one?
At its core, data science helps your company make decisions on product and operating metrics. It does this via data products and decision science – improving product performance, building prediction models, affinity maps, and cluster analysis.
But data science is just one tool. Business intelligence and analyst functions can also help with operating metrics, albeit with more basic toolsets of SQL and Excel. Whether you use one or the other depends on your company’s data infrastructure and event volume – and hiring data scientists too early can be like trying to crack a nut with sledgehammer. Except that this particular sledgehammer will feel understimulated, underappreciated, and probably end up quitting.
Not recognizing the distinction can lead to premature adoption of a data science team at high resource costs, hurting your business and limiting your data scientists.