Data science is a team sport: How to choose the right players

Scaling data science requires more than just data scientists, said the experts in a panel hosted by MLOps firm Domino Data Lab
Written by Stephanie Condon, Senior Writer

Building deep and ongoing data science capabilities isn't an easy process: it takes the right people, processes and technology. Finding the right people for the right roles is an ongoing challenge, as employers and job seekers alike can attest. 

"The people part is probably the least well-understood aspect of this entire equation," John Thompson, global head of advanced analytics & AI at CSL Behring, said during a virtual panel discussion on Thursday. 

As the head of analytics at one of the leading international biotechnology companies, Thompson oversees data science teams that tackle a wide range of initiatives. He and the experts in the virtual panel, hosted by MLOps firm Domino Data Lab, agreed that scaling data science requires more than just data scientists. 

To kick off data science initiatives at CSL Behring, Thompson says he starts with a "skeleton team you need for a project to be successful." That typically includes engineers, data scientists, a UI or UX data visualist and subject matter experts. 

A successful data science team also needs a leader who can make sure projects stay focused on business objectives. 

"If we're saying data science is a team sport, you don't just need all the players; you need a coach," said Matt Aslett, research director for the data, AI& Analytics Channel at 451 Research.

It's clear that a complete data science team comprises more than just data scientists -- but it isn't necessarily wise to consolidate data science teams within an IT department, added Nick Elprin, CEO and co-founder at Domino Data Lab. 

"One of the things we've seen among companies we work with that are most successful is they closely align those teams with business objectives," he said. "How you guide their work and prioritize, the closer you can get that to the core company objective, the more likely you are to [be successful]. When you move more into IT, you get further away from core objectives."

Managers also need to consider how their teams are organized when they're hiring, Elprin said. They should ask, he said, "What types of skills are you going to make core to the role, and what will you augment with other people you'll collaborate with?

"Companies have success [building data science teams] with folks who know stats and basic programming and augmenting them with people who know devOps or other engineering capabilities," Elprin added.

Meanwhile, it's important to consider when professional data scientists are truly needed versus tools that purport to "democratize" data science and machine learning. 

"It depends on the nature of the problem you're pointing your data science and machine learning folks toward," Elprin said. "For commoditized problems, some of the auto ML solutions can be effective. If you're talking about a problem unique to your business or core to your differentiation, you need more of... the flexibility that comes with developing your own proprietary models and using the power of code to express those ideas."

Finally, advancing impactful data science projects requires buy-in from executives, Thompson noted. 

"The real challenge is the macro-level change management process; it's not really about the data science process," he said. To realize the full value of a full data science initiative, he said it's important to convey to executives that "in the end, it's going to drive change. You need to be ready to drive change... if you don't want to do that, maybe we should do a project, not a program."

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