3 Key Trends Shaping the 2017 Data Science Hiring Market
This post is an adapted excerpt from our newly-released report, The Burtch Works Study: Salaries of Data Scientists 2017, which examines updated compensation and demographic data for data scientists.Download the full study to see how data science salaries vary by experience level, region, industry, and residency status, plus our insights on how these trends will shape the future of the market. Four and a half years after Tom Davenport and DJ Patil declared data scientist to be the “sexiest job of the 21st century” in the Harvard Business Review, Burtch Works is releasing its fourth annual salary study for the profession. Fresh graduates, academics, and career changers alike all seem eager to jump in, and, over the years since our first study was released in 2014, we’ve watched several major trends take shape:
- More junior-level data scientists available
Most noticeably, to anyone familiar with the “Big Data” hype, but especially to us as quantitative recruiters, is that the overall supply of junior-level data scientists has increased. In our 2016 study, we highlighted that degree and enrollment levels for the STEM fields have been increasing over the past few years. Taken together, math/statistics, engineering, computer science, and natural science degrees account for the academic backgrounds of 80% of data scientists, and the increased interest in all of these fields of study is undoubtedly having an effect on the market (see webinar for details).
- More early career data scientists opting for Master’s degrees, not PhD’s
In last year’s report we also noted that individual contributors with 0-3 years’ experience, where demographic shifts often provide the most likely snapshot of where overall market trends are headed, started to tip towards more Master’s degree holders, as opposed to PhD’s. As we predicted, that shift has continued this year.We believe that this is a result of more graduates opting for terminal Master’s programs, looking for a faster path to the workplace to capitalize on the data tidal wave. We’ve also spoken with some individuals who have dropped out of their PhD programs, looking to pursue careers in data science immediately rather than spend several more years in academic research. Although that trend is not as widespread, many students are clearly intrigued by data science careers.
- More traditional predictive analytics professionals transitioning into data science roles
Another substantial tributary feeding into the data science talent pool is other professionals in predictive analytics. Although we consider predictive analytics to be closely related to data science, for our purposes we generally distinguish between the two because data scientists have the tools and skills necessary to work with unstructured or streaming data, and so they will possess more computer science skills.
As we’ve noted in our previous reports, this variation in skillsets results in significantly higher compensation for data scientists when compared to predictive analytics professionals that work with structured data, so we’ve kept the two groups separate.However, as we pointed out in last year’s report, The Burtch Works Study: Salaries of Predictive Analytics Professionals, more traditional analytics professionals have been learning typical data scientist tools and developing the skills necessary to transition into data science roles. Now, the fields have begun to blend even more, and we expect the amalgamating to continue as analytics professionals take advantage of MOOCs (massive open online courses), bootcamps, microdegrees, in-house training, and other programs to supplement their current skillsets.This increase in supply, shift towards Master’s degrees instead of PhD’s, and blending of traditional predictive analytics professionals and data scientists is having an interesting effect on data science salaries. For full compensation and demographic information on this in-demand group be sure to download the full report!