"The reason I'm skeptical is because I believe in the science portion of our field's name. One of the primary things that separates a data scientist from someone just building models is the ability to think carefully about things like endogeneity, causal inference, and experimental and quasi-experimental design."
What exactly is a 'data scientist'? Shouldn't scientists be the ones analyzing their own data, instead of 'data scientists'?
My reply to this would be that everyone has to do some sort of statistics and modeling in their studies.
Data science tends to be more about having good software engineering, understanding how to interface with production systems to pull data out, and enough modeling experience, almost specializing in it, to be able to make inferences about different kinds of business activities affecting revenue, or other parts of the business.
You can get away and even grow in to a data science role as a statistican or software engineer (probably leading towards data engineering more than data science).
Source: students of mine get hired by companies like facebook[1].
So: to summarize, data scientists get hired for roles at companies to focus only on modeling, data quality, and data advocacy, and assisting product roles.
The first chapter of "Practical Data Science with R" answers exactly this. [1] In summary, you're designing and running experiments to help a business, so you're generally working for a non-scientist.
What exactly is a 'data scientist'? Shouldn't scientists be the ones analyzing their own data, instead of 'data scientists'?