[dworin]

It's very hard to find people with both deep domain knowledge and deep math/statistics knowledge, in the same way that it's often hard to find people with deep programming knowledge and deep business knowledge.

We solve the latter problem by having business analysts or product managers that "get" the technology enough to provide direction, even if they wouldn't be effective implementing it themselves. I think there's a next phase where, as we try to do data-science at scale, we look for a similar role that deeply understands the business and knows enough about the analytical techniques to define the problem and work with a team of specialists to figure out the best analytical approach.

People talk about data science teams being multifunctional - with programmers, data engineers, data scientists, and designers - but we always leave out the role for someone with deep business expertise and shallow but meaningful data science expertise.

[plusbzz]

As the author of the OP, I must say this is very well put. Part of the problem is that there is no fixed 'role' for the person with the 'deep business expertise and shallow but meaningful data science expertise'. In my experience, it could be a bunch of different people. When I was in a network security startup, this expert would typically be a malware analyst. In other companies, depending on the project, it could be someone from Product, Sales or Marketing. Similar to designers, a data scientist is expected to figure out who the main stakeholders are and get them engaged in the process, instead of the business stakeholder being part of the data science team per se.