Originally published on Forbes.
As we consider what parts of the labor market thrived in 2016 and what will continue to gain momentum in 2017, it’s hard to miss the demand for data scientists: people who are, according to SAS, “part mathematician, part computer scientist, and part trend spotter” and “straddle both the business and IT worlds” to analyze complex business problems using large datasets.
In this data-dominated era, everything and everyone produces a digital paper trail. If businesses want to gain an edge, they need to be able to tap into those large, elusive data sets to make better decisions about how products are built, markets are found, clients and employees are supported, and sales are generated. Hence the need for data scientists. It has become crucial to employ analysts who can turn all that raw information into actionable insight—so much so that many positions, ranging from financial analysts and computer systems analysts to statisticians and economists, are increasingly engaged in the art of data science.
According to data produced by Emsi and CareerBuilder, there were, on average, 2,900 unique job postings active per month for data scientists over the past nine months. (NOTE: The total number of job postings is, of course, much higher because employers advertise on many different job sites. Emsi has de-duplicated those postings to the real number of locations / businesses posting ads for those jobs.) The top states for data science job postings are California, Washington, New York, Virginia, and Massachusetts, while the top metros are San Jose, Seattle, New York, Washington, D.C., Chicago, and San Francisco.
Consistent with the digitalization of the labor market, the companies looking for this talent represent virtually every industry. If you want a job at a major, fast-growing company, look no further than data science. Oracle, Microsoft, Amazon, Apple, Booz Allen Hamilton, GE, State Farm, Walmart, Facebook, United Health Care, Aetna, AT&T, Intel, IBM, Nielsen, KPMG, eBay and many more all show up prominently in job postings for data scientists.
So, what does it take to become a data scientist?
If you’re a jobseeker, how might you become a data scientist? If you’re a hiring manager or employer, what are other businesses looking for and what knowledge and skills (or college majors) are producing data scientists?
When we consider the full text of what employers are looking for, we notice an interesting trend. Yes, hard skills are obviously important: analytics, research, machine learning, statistics, Hadoop, and more. But we also see heavy emphasis on many soft skills: the ability to lead, communicate, learn, think critically, work on a team, and be an ethical and reliable worker. In other words, businesses need human data scientists—not robots who process data. They need solid, well-rounded workers with both foundational skills and technical talent. Data scientist is very much a hard-skill career built on a lot of soft skills.
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