Two new surveys tell us a lot about both the supply and demand sides of the hot market for data scientists, “the sexiest job of the 21st Century.”
On the demand side—the challenges of recruiting, training, and integrating data scientists—we have the MIT Sloan Management Review and SAS fifth annual survey of 2,719 business executives, managers and analytics professionals worldwide. On the supply side—the talent available and what salaries it commands—we have the second annual Burtch Works Study, surveying 371 data scientists in the U.S. (see also the video presentation at the end of this post).
The median salary of a junior level data scientist is $91,000, but those managing a team of ten or more data scientists earn base salaries of well over $250,000, according to Burtch Works. Supply is still tight and top managers enjoyed over the last year an eight percent increase in base salary and median bonuses over $56,000. When changing jobs, data scientists see a 16 percent increase in their median base salary.
Who are these data scientists that are so much in demand? The vast majority have at least a master’s degree and probably a Ph.D., and one in three are foreign born. But with a younger generation of data scientists, freshly minted from more than 100 graduate programs worldwide, the median years of experience dropped from 9 in 2014 to 6 in 2015.
As data science is increasingly adopted by all companies in all industries, the proportion of data scientists employed by startups—the firms that have dominated the application of big data analytics— declined from 29 percent in 2014 to 14 percent in 2015.
It is the mainstreaming of data science and the specific challenges of acquiring and benefiting from this still-scarce talent pool that is the focus of the MIT Sloan Management Review survey. Four in ten (43%) companies report their lack of appropriate analytical skills as a key challenge but only one in five organizations has changed its approach to attracting and retaining analytics talent.
As a result of the scarcity of data scientists, 63 percent of the companies surveyed are providing formal or on-the-job training in-house. “One big plus of developing analytics skills among current employees,” says the report, “is that they already know the business.” These companies are also doing more to train existing managers to become more analytical (49%) and train their new data scientists to better understand their business (34%). Still, half of the survey respondents cited turning analytical insights into business actions as one of their top analytics challenges.
To better manage these challenges, the study recommends giving preference to people with analytical skills when hiring and promoting, developing analytical skills through formal in-house training, and integrating new talent with more traditional data workers.
“Infusing new analytics talent without proper support and guidance can alienate traditional data workers and undermine everyone’s contributions,” says the report. Yet only 27% of companies report that they successfully integrate new analytics talent with more traditional data workers. So even after managing to find (and pay for) the data science talent, there is no guarantee for the desired results, either because of the lack of understanding of the business by the new recruits, resistance from current employees engaged in data preparation and analysis, or failure to translate new insights into meaningful action.
Many companies have responded to these challenges by creating new roles and responsibilities and devising new organizational structures. The report points out that the range of analytics skills, roles and titles within organizations has broadened in recent years. What’s more, new executive roles, such as chief data officers, chief analytics officers and chief medical information officers, have emerged to ensure that analytical insights can be applied to strategic business issues.
Whether the work is centralized or decentralized, data science and analytics should be perceived and managed by companies as a professional function with its own clear career path and well-defined roles. Tom Davenport asked in a recent essay: “When was the last time you saw a job posting for a ‘light quant’ or an ‘analytical translator’? But almost every organization would be more successful with analytics and big data if it employed some of these folks.”
Davenport defines a “light quant” as someone who knows something about analytical and data management methods, and a lot about specific business problems, and can connect the two. An “analytical translator” is someone who is extremely skilled at communicating the results of quantitative analyses.
Data science is a team sport that requires the right blending of people with different skills, expertise, and experiences. Data science itself is an emerging discipline, drawing people with diverse educational backgrounds and work experiences. Typical of the requirements for a graduate degree is what we find in a recent announcement from the University of Wisconsin’s first system-wide online master’s degree in data science: “The Master of Science in Data Science program is intended for students with a bachelor’s degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst, information technology analyst, database administrator, computer programmer, statistician, or other related position.”
As with any team sport, there are stars that are paid more than the average player. According to Glassdoor (HT: Illinois Institute of Technology Master of Data Science program), the average salary for data scientists is a bit more than what Burtch Works reported, at over $118,000 per year. (By the way, Glassdoor reports the average salary for statistician is $75,000 and $92,000 for a senior statistician).
It’s possible that the Glassdoor numbers include more of what Burtch Works calls “elite data scientists.” Do we know who is in the elite of top data science players? The closest we get to identify the MVP of data science is the Kaggle ranking of the data scientists participating in its competitions. Currently, Owen Zhang is number one. Zhang says on his profile that “the answer is 42” and his bio section tells us that he is “trying to find the right question to ask.” He lists his skills as “Excessive Effort, Luck, and Other People’s Code.”
Zhang is currently the Chief Product Officer at DataRobot, a startup helping other data scientists build better predictive models in the cloud. He is also yet another example of how experience and skills still matter today more than formal data science education. His educational background? Master of Applied Science in Electrical Engineering from the University of Toronto.
Originally published on Forbes.com
This Burtch Works webinar provides highlights from the 40+ pages of compensation and demographic data in the report, which is available for free download here: http://goo.gl/RQX1xd