Ever since HBR published an article in 2012 declaring data scientists to be the sexiest jobs of the century, in 2013, and the McKinsey Global Institute (MGI), published a report that by 2018 the United States will experience a shortage of 190,000 skilled data scientists, everyone has been rushing in to collect and hoard data scientists. Smart job applicants started including “data scientist” as a skill in their resumes (and were rewarded with exciting job offers), regardless of their actual qualifications.
And all this, even before organizations had figured out what problems to solve with these highly qualified individuals, and what other investments needed to be made to make them productive and effective.
Competition for talent is likely to be robust for people with experience in healthcare data and analytics. A recent study mentions that 37% of the respondents indicated a lack of qualified staff as a factor in adoption rates for analytics. Another study highlights some nuances to this talent market:
–Data scientists have a median of only six years of experience, but are highly educated (92 percent have at least a master’s degree, 48 percent have a PhD), overwhelmingly male (89 percent), and a disproportionately large number are foreign-born (36 percent).
–Over one-third are employed on the West Coast (36 percent) and almost half work for firms in the technology and gaming industries (43 percent).
The study indicated median compensation of data scientists can range from $91,000 with one to three years of experience up to $250,000 for managers leading teams of 10 or more.
What is wrong with this picture?
–There are several key factors impacting efforts to increase adoption rates for analytics, and one of them is the talent shortage.
–The talent pool for data scientists doesn’t scale well, at least in the United States.
— In addition, it appears that younger workers and recent college grads prefer to work in smaller organizations that provide more challenging data analytics problems to solve.
The impact of these forces will cause an inevitable reversion to the mean (to use data scientist-speak). Translation: inflated salaries will drop back to average levels pre-inflation when alternate sources of talent supply are identified and brought on stream.
Big companies are latching on to this as well.
The final nail in the coffin will be hammered in by enterprises and business leaders themselves, who are going to come to their senses and put a stop to the hiring madness when the returns on these investments don’t add up to the promises. Read More…