“Demand for data scientists is off the charts,” so says the LinkedIn Workforce Report from August 2018. “Data Science skills shortages are present in almost every large U.S. city. Nationally, there is a shortage of 151,717 people with data science skills with particularly acute shortages in New York City (34,032), the San Francisco Bay Area (31,798) and Los Angeles (12,251).”
Smith Hanley Associates has been recruiting in advanced analytics for over 30 years. Here’s what Data Science and Analytics Practice Lead Recruiter, Paul Chatlos, gives as the reasons our clients are struggling in hiring a data scientist.
Small Talent Pool or High Demand, Low Supply
According to statistics from the University of California, Riverside a little less than one third of the top 100 Global Universities offer degrees in Data Science. Of these 29 universities, only six offer data science programs at the undergraduate level, the rest are postgraduate degree programs. These programs graduate an average of just 23 students. Small programs from a limited number of universities means less than 700 graduates annually. Not enough to make a meaningful dent in the nearly 700,000 openings by 2020 that IBM predicted in 2017. Globally the demand for data scientists is projected to exceed supply by more than 50% by 2019. With more than 40% of companies believing their difficulty in hiring a data scientist is hindering their ability to compete, it is no wonder over 60% of businesses train their staff in-house.
Quickly Evolving Field
The list of skills and software that define a top data scientist is an ever-evolving target. Companies want in-depth experience in a wide breadth of skills. It is hard for any candidate to keep up, and for any company to succinctly define what they need in their data scientist. Hiring someone with everything, isn’t feasible and the ideal job description may change within every six month time frame. Candidates look for companies that are open to a smart, sharp data scientist learning more while on the job, not bringing everything with them to start…an unrealistic expectation in this quickly changing/growing field.
Many companies want champagne on a ginger ale budget. Companies haven’t brought their pay grades in line with what data science leadership and data science soldiers are worth. In 2018 the average salary for a junior level data scientist was $115K and those managing a team of 10-15 members can demand salaries as high as $350K. Meanwhile the median years of experience for a data scientist dropped from 9 years in 2014 to 6 years in 2015. Top managers fail to understand that hiring a data scientist is not about saving money and reducing costs, but about better executing the business model–perhaps even changing it.
High Competition for Talent and Unrealistic Expectations
Hiring a data scientist with 3+ years of experience could mean competing with 10-15 other companies that candidate is looking at and probably 2 or 3 other job offers, particularly if the data scientist has experience in Artificial Intelligence. Companies are eliminating viable candidates based on lack of extensive industry experience, requirements for in-depth experience with multiple data science skills and a lack of understanding of the software and data support needed for the data scientist to do their job. One data scientist said, “If you’re hiring me, its’ because I can learn anything, and I will have to learn all kinds of things to do this job well.” Expecting the candidate to have every expertise before hiring is a hard find and a candidate won’t be interested in a job they can’t grow with. The data scientist went on to say, “I’m reluctant to apply everywhere, because so few places seem to know how to spot someone like me when my resume lands in your inbox.”