Despite all the hullabaloo about the need for data scientists, hiring demand has not matched the hype to this point, recruiters have told me.
For one thing, those people are hard to find and come at a steep price. Many organizations are still trying to figure out what a “data scientist” is and does. In a piece at Network World, Greta Roberts, CEO at Talent Analytics Corp., groups the job functions into four areas, based on a survey of folks in these roles:
The skills required for these positions include experience with advanced math, statistical analysis (including tools such as R, SAS and Stata), programming (including languages such as C, C++, Python and Java), SQL databases, platforms such as Hadoop and MapReduce, data mining and modeling, data visualization, creativity, communication skills and business understanding.
The ability to communicate what the data is saying and how the company could act on that knowledge is key.
Jill Dyché, vice president of Thought Leadership at SAS, however, told my colleague Loraine Lawson that it’s unrealistic to expect to find all these skills in a single person. She said:
“What we're finding on the ground with our customers is the expectations for that individual role are so lofty, it's just become completely impractical to expect any one person to understand the data, understand the data sources, understand the data integration rules, understand the business rules, understand the meta data, understand the data access, understand data privacy, understand data security, understand how to cleanse the data, et cetera, et cetera.
It's a fun thing to talk about, but on the ground, the specifics of the role are very unclear. In the worst-case scenario, we're setting people up for failure.”
No wonder that many companies are tackling analytics by committee or pulling together a range of skills into a Big Data competency center.
One thing experts agree on, however, is that this is a huge career opportunity for those who can get into this field.