Performance Metrics for Data Scientists

According to QuantHub, there was a shortage of 250,000 data scientists in 2020 concurrent with steadily increasing demand. The US Bureau of Labor Statistics estimates a growth rate of 31% for data scientists and related positions through 2029. There are over 57,000 data scientist job postings on LinkedIn at the moment. All of this helps underscore that 86% of companies find it challenging to hire qualified IT talent.

Data scientists are often expected to have a Master’s degree and several years of experience. But, companies also want industry professionals familiar with their particular market dynamics. And they want some coding skills. And, in many cases, despite the lockdowns, candidates should either live locally or be willing to relocate. And then… Mick Jagger just ran up to me and slapped me like the Orangeman from a Tango commercial singing, “You can’t always get what you want, but if you try, sometimes – you get what you need.”

As the Toptal data scientist job description above more succinctly describes, data scientists are,
“x% scientist, y% software engineer, and z% hacker.” Only, a lot of times, it’s also necessary to add, “w% industry expert.” We can simply say that many data scientists are still in the process of learning and developing their job-specific skills. So, while they may be challenging to find, they are by no means unique with respect to the need and value of continued education and training.

Moreover, companies with data scientists are likely to have more than one. They are very likely to be part of a team with other software engineers and developers. Companies with the need for more data scientists are often trying to source from within – perhaps training their engineers to be scientists while helping developers become engineers and engineering managers. So, knowledge sharing and team development are also factors that come into play.