Is the Data Science Team Equation Balanced?

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In a January 2017 blog, I mentioned types of data scientists and described some of the technical skills they possess. Today I want to delve a bit deeper into a Field Guide to Data Science that talks about qualities of a great data science team. This guide talks about how to build a strategic data science capability within the organization by having the right combination of talent. It also mentions how it is a team sport that takes the right balance of skills from computer science, mathematics and knowledge of the domain.

I love the creativity in comparing balancing the ‘composition of a Data Science team’ to balancing a chemical reaction. In the particular equation example above, CS is someone with Computer Science skills, M is someone with math expertise and DE is someone with domain expertise. The data science team can accomplish its goals if it has 4 computer science people, 5 math people and 1 domain person. In this equation, a team of five people, some of those people obviously having skills in multiple categories described above, is what the company needs and what is considered balanced.

My follow-up question to this equation is how does one arrive at the number of people needed objectively and with data? The field guide says that ‘building a data science team is complex’ but frustratingly offers no concrete ways to overcome this challenge. There are several existing viewpoints from Data Science Central, MIT Sloan Management Review and Data Camp that describe the necessary functions and skills that make up a great team but not one says how many of each type of skill would create an environment for success.

Since I’m still in the process of becoming a data scientist, I have yet to work on a data science team. If you have experience in the field, I’d love to hear your thoughts as to how to determine the balanced data science team. Until then balancing the process of balancing the equation seems elusive and a bit random to me.

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