2016 marks the third consecutive year that Burtch Works has published The Burtch Works Study: Salaries of Data Scientists, about the “elusive kingpins of the Big Data movement”. The enthusiasm for data-driven strategy has continued to increase since our initial study in 2014, and readers of the report are likely well familiar with some of the effects this has had on the quantitative hiring market.
2016 marks the third consecutive year that Burtch Works has published The Burtch Works Study: Salaries of Data Scientists, about the “elusive kingpins of the Big Data movement”. The enthusiasm for data-driven strategy has continued to increase since our initial study in 2014, and readers of the report are likely well familiar with some of the effects this has had on the quantitative hiring market.
We’ve seen a number of interesting use cases for data science in an increasing variety of industries. Internet of Things industrial applications have emerged, which can involve using sensor data to develop predictive maintenance capabilities to anticipate equipment failure. In entertainment, casinos use real-time customer data to build and optimize their loyalty programs, and video game companies track player behavior to improve engagement as well as develop future offerings. In the behavioral health area, data science applications can be used to personalize medical treatment, coordinate prescriptions, and monitor for changes in behavior. And, of course, the ability to use data science to personalize advertising continues to offer copious benefits for marketing teams.
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One side effect, however, of this marked increase in data science use cases is the near-ubiquitous use of the “data scientist” title. The overwhelming popularity of the title and the newness of the discipline have resulted in the term appearing on a myriad of job descriptions, resumes, and LinkedIn profiles, whether or not the role or professional possesses the full range of data science skills. Examples of relevant skills might include implementing machine learning algorithms, wrangling unstructured data, or using tools like Hadoop, among others.
On the recruiting front, a constant refrain from clients has been the struggle to find data scientists who can balance their strong technical skills with the business acumen and domain knowledge needed to make an impact on business goals. The ability to glean information from massive datasets and then translate how those insights become actionable for the business has always been important, but is even more so now that leaders are looking to capitalize on the value of their data and demonstrate a return on their investment.
With all of the attention that data science has received, students have been flooding into the field. Degree and enrollment trends show increases in statistics, mathematics, computer science, and engineering graduates – all top educational backgrounds for data scientists – and students can’t wait to get to work, as evidenced by the increase in the percentage of professionals with Master’s degrees (as opposed to Ph.D.’s) at the entry-level (see the report for details). Additionally, there are many professionals looking to transition into the field from other career paths.
With some of the hype beginning to give way to a more “business as usual” attitude, these next few years will be a proving ground for newly forged data science teams, Master’s programs, bootcamps, startups, and for new and established data scientists.
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