A note to technical recruiters: There’s a difference between a data scientist and a Python programmer who knows some data science frameworks
We’ve recently heard from a number of technical recruiters who’ve been burned. They were tasked with finding technical talent who could cover the standard data science bases: Python (or R), Pandas, scikit-learn, and maybe even Tensorflow or Keras. So they went out and found expert developers with these skills. The problem was that in actuality their organizations didn’t need developers—people who could write code. They needed data scientists—experts who could understand the business, identify where analytics could provide value, and could then write code.
There’s a role for both. Sometimes data scientists need supporting technical arms and legs (and brains) to bring their ideas to life (i.e., developers, programmers). And, over time, these programmer types might grow into full fledged data scientists. Just don’t make the mistake of confusing one for the other. If you need to recruit someone who can interpret your business’s needs and then drive the coding to attack those needs, make sure your initial filters, and your subsequent interview questions, screen for those skill sets.
All of this points the importance of technical recruiters having a clear vision for new roles from the very start of the process—the more specifically you can define your goals for the position and the functions you hope candidates will be able to take on, the more likely you are to find what you are actually searching for.
Are you a technical recruiter struggling to find and screen data science and data engineering talent? If your current tools and vendors are working for you, congratulations! But, if you’re not getting where you need to go, if you’d like to work with a team of experienced experts who won’t waste your time, who know how to find and screen top data science talent, let’s talk!
We’ve got years of experience finding top analytic talent. In fact, in just the last month, we’ve placed a number of experts including both a director of data science in California and a manager of data engineering in Michigan.