For those leading Data Science teams, here are 6 essential competencies that separate juniors from seniors in a robust and objective way.
In 2021, 365 DataScience carried out a study of 1,000s of Linkedin profiles to understand trends in the data science discipline. A couple of points that really stood out were that “Very few individuals (less than 2%) have stayed at the same job for more than 5 years” [1] and “The median time spent on the job by a data scientist in our study was 1.7 years” [1]. Fortunately, I haven’t seen this turnover in my teams, but I know many data scientists, and most argue that ‘lack of role clarity’ is one of their top 3 challenges. If you feel this is happening to you or in your team, I hope this article helps you build a competency framework which is adaptable, fair and robust to provide answers for this ‘lack of role clarity’.
PS: All images are authored by me, unless otherwise specified.
A competency framework is a means by which teams communicate which behaviours are required, valued, recognised and rewarded with respect to specific roles or levels. For example…