Sure, teams are essentially what drives data science. We have them here at the company I work for. We always pair data engineers (i.e.: DBA-like, ETL devs, etc) with data scientist to help ensure success. But make no mistake, just because it's a team effort does not mean the data scientist is voided. You still need that statistician on the team that is fluent in statistics, probability, and complex math who also has the domain experience. This is the separator from someone who is a statistician and a data scientist -- the domain experience. Likewise, that's also the difference between me (the data engineer) and the scientist, because like most tech resources that wrangle data, we do not specialize in statistics and probability in our day-to-day.

While I know it's easy to shove this off like it's just another trendy title that is doing the same things other have already been doing, there is a pretty clear difference between machine learning and canned reporting just like there is a pretty clear difference between using SSAS Data Mining versus R for predictive analytics. You can't just easily lob these guys into the same bucket like their roles do not exist. If you're going to employ machine learning techniques then you will likely need a data scientist to exist on your team regardless of your opinion. That are entrust your programmer or DBA to be good enough in this field even though it's not their specialty... :crazy: