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In its recently released 25 Best Jobs in America for 2016 report, Glassdoor listed data scientist as number 1 career -- but it wasn't just top of the list for tech. It topped every industry. The report cites 1,736 openings in the field, a median base salary of $115,840 and an overall job score of 4.7 out of a total 5, which are all promising stats for this quickly growing career path. But the fast-paced growth for data science jobs has been met with a severe lack of qualified candidates. And businesses that do hire data scientists often have no idea how to effectively utilize their skills.
In fact, McKinsey Global Institute looked at big data across nearly every industry and found that as of 2009, nearly every company with more than 1,000 employees in the U.S. averaged 200 TB of stored data -- and that was 6 years ago. Data mining has significantly increased since 2009, and as of 2016, every tech company collects massive amounts of data on users. The study also revealed that by 2018, the U.S. could face a talent gap of 140,000 to 190,000 qualified data science workers.
Tye Rattenbury, director of data science at Trifacta has watched the role of data scientist evolve as companies figure out how to properly use these employees. Rather than hire the data scientists and figure it out later, businesses need to go into a data strategy with a clearly developed plan to get the most out of their investment, Rattenbury says.
Define the job description
The expectations of a data scientist are not only to manage data, but also to interpret data and effectively communicate it to others. But most data scientists are stuck in maintenance mode -- organizing and collating data, rather than actually spending time analyzing it, according to Rattenbury. "As with all new and exciting things, there is a lot of ambiguity around what is possible and what the best practices really are. The big winners (both individual data scientists and the companies that employ them) will have the discipline to see through the hype and hone in on the activities that can and do add value," he says.
Aaron Beach, data scientist at SendGrid, says the best approach to building a data science role or department isn't one that bogs scientists down with information overload -- but instead is built around how data needs to be analyzed for the company's benefit. "The strategy should be defined in terms of a process for how raw data is translated into actionable information for decision makers, not in terms of which raw data is or isn't useful," he says.
Another way businesses can get more out of their data scientists is to focus on building the department in a way that doesn't just reflect lofty expectations of data, but is based off the actual needs of the business. For example, a business should know before the hiring process begins how many data scientists their business will need, but that can't be determined without first having a clear strategy that outlines what data is needed and how it needs to be translated.
"Data science is an immature, diverse and vaguely defined 'job'. As such, it's impossible to say how many or what kind of data scientists are needed by a company until they clearly define the job as it relates to their business. At SendGrid, we define the data science job and its career path as it relates to our product and engineering process -- this helps answer the questions of how many data scientists we need and directly defines the skill set those employees will have," says Beach.
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This story, "How to define the evolving role of data scientist" was originally published by CIO.
Source: ITNews
