[tansey]

Perhaps more precisely, they're "statistical engineering" jobs. A machine learning PhD can derive an algorithm and provide you with a reassuring bound or guarantee regarding performance in terms of runtime, convergence, etc. They have to be able to understand not just the volume of the data but also how to trade off accuracy for speed, and myriad other constraints.

IMO, the "data science" label is too broad to properly differentiate statistical engineers. A fine definition for a data scientist is someone who runs experiments on user/company data and can assess the results. It's important work, but you don't need a PhD in stats or ML to do basic hypothesis testing.

You could simply call them "machine learning" experts, but that could be a bit too academic. People who are focused narrowly on theory or niche areas may be experts in ML, but they may also never do anything outside of running matlab simulations. It's unlikely that those people will make very good statistical engineers since they may never have had to think about the challenges involved in scaling algorithms.