\>>How do I quickly learn about DS and their role + output?
Andrew Ng has just released a course which may prove instructive
And a quick overview:
There are lots thoughts on what DS do all day, a definition I came across recently is DS produce analysis to inform business decisions, ML Engineers build working models in production environments. Best hold individual discussions to define roles and work out how they want to develop them.
n.b. follow towardsdatascience.com
\>>I’ve oddly managed (people) engineers before, should I (people) manage them similarly?
Difficult to define as you are taking on an existing team. Find out from them what works and what doesn't, aim to improve incrementally. There is a lot to digest from this series of posts, but well worth the effort:
\>>How do I manage their work/stories/chunks of work?
and as a very interesting counterpoint, Paco Nathan explores the role of curiosity in data science work
\>>How do I understand how they interact with back-end systems, APIs, front-ends, etc?
There must be documented DS process that includes a focus on reproducability of research. This will also help with onboarding new hires. Some ideas:
\>>Is there an opportunity to change how DS are typically treated and utilized?
I guess this depends on organizational maturity.
\>>Which organizations are known for leverage DS well? (And document their culture + principles)
See MS TDSP above
\>>Why does this seem like a great new challenge (like the one I was seeking) and simultaneously my “first day of college” again?
This is a pointless concern, don't give it a second thought, just climb on-board and enjoy the ride.
\>>I’m clearly forgetting dozens of things, care to thrown tips, articles, and general ideas at me?
I hope these links will give you lots of food for thought. But a proverb for you:
'He who asks a silly question is a fool for 5 minutes. He who doesn't is a fool forever'.
Finally, aim to be a servant leader.