5 Tips to Hire and Retain Top Data Scientists

Today’s organizations are being reshaped by data. And they are investing in the tools and talent to unlock its potential. But, turning this potential into insights that drive real business results is a tall order. One thing is clear: Data science is a huge part of this equation.

Our recent article, 5 Trends in Data Science, gives tons of insight into where the fascinating field of data science is today and where it is headed. As a follow-up to that article, today’s article is focused on how you can hire and retain top data scientists.

Here are 5 important tips to consider:

Have a compelling value proposition for a data scientist to join your team

Otherwise, we suggest you don’t compete!

·     Even if you land a few hires, you will lose them to the next sexy or higher-paying option.

·     With over 200,000 unfilled data scientist roles currently open—and growing each month—the competition is fierce.

With over 200,000 unfilled data scientist roles currently open, the competition is fierce.

Pay is table stakes but not enough to close the deal

The best data scientists want variety and real problems/challenges to solve and to be valued by the company—not stuck in an organization that does not understand how to utilize their skill set.

  • Our experience is that Big Data teams are often kept to the side of the business and are being run by non-quantitative leaders. That is not a winning formula.
The best data scientists want variety and real problems/challenges to solve and to be valued by the company.

Challenge Big Data teams in a Freakonomics way

They will love the challenge, and you will be surprised by the type of answers they will develop.

  • In the book, Freakonomics, written by Dubner & Levitt (the latter being a world-class economist), they answer many cool questions. It is a standard approach taken by a data scientist, developing hypotheses and accessing and crunching data to prove or disprove the hypothesis.
  • One question they asked was, “Is there Cheating in Sumo Wrestling?” I wondered how they would prove this. By the end of that section, they created such a compelling case, that there is no question there is cheating in Sumo wrestling!
You will be surprised by the type of answers that your big data teams will develop.

Hire for cultural fit and an aptitude, and stop grinding on less important criteria

Don’t shut out great candidates because they are missing a couple of things that you are looking for.

  • Many candidates are disqualified due to a lack of industry and vertical expertise. Many of the best data scientists that come from traditionally data-driven industries (i.e., financial services) are used to getting their hands dirty with data to drive marketing and operational decisions.
  • With the right level of oversight and training, a data scientist can be productive immediately and will get better with time.
With the right level of oversight and training, a data scientist can be productive immediately and will get better with time.

Keep the main thing, the main thing—find ways to get top talent working for you

An analytical investment should produce at least a 12:1 ROI. So, the main objective is to find ways to get people on the team.

  • Secure a small or large team from a data science firm on a retainer basis for a defined time or project.
  • Try an “evaluate-to-hire” engagement, meaning before you hire, test the data scientist to see if this is a good fit for both of you.
  • Be flexible with face time. Given today’s cloud-based platforms and how hard it is to find great talent, make it easy for talent to work partially or fully remote.

An analytical investment should produce at least a 12:1 ROI.

So, the main objective is to find ways to get people on the team.

Drive success with top talent

While the competition is tough when it comes to finding a data scientist, there are great opportunities out there to work with top talent. The best are unique, innovative thinkers who will uncover ways to drive value for your business.