Data science is a team sport: How to choose the right players | ZDNet
Scaling data science requires more than just data scientists, said the experts in a panel hosted by MLOps firm Domino Data Lab
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.
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.
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