You're not alone. I have grumbled about it for years. I know Hilary Rosen and others have advocated for the term and I am very sensitive to the hardships endured by working in such an interdisciplinary manner, but it really is a goofy name.
It has always seemed to me just an excuse to run away from the "Artificial Intelligence 2.0" moniker and all the negative connotations that would imply. I dislike the label "Data Science" because there is not really much "Science" with a capital-S being done by people who adopt the moniker and with whom I have had chance to meet.
I have always thought that "Knowledge Engineer" was a more descriptive and useful term for what they actually do. The more abstract you get it seems to fall into the field properly known as Computational Mathematics.
All good, but I think statistics as I understand it, is only a facet of the work involved. And the outcome or goal of their work seems generally to be the creation of a knowledge-based system for predictive analysis; to derive ontological meaning from numerical data (mostly about humans and human activity).
Not a bad article, if a little short, on wikipedia kinda captures it for me [0]:
* Assessment of the problem
* Development of a knowledge-based system shell/structure
* Acquisition and structuring of the related information,
knowledge and specific preferences (IPK model)
* Implementation of the structured knowledge into knowledge
bases
* Testing and validation of the inserted knowledge
Right, but the question is whether statistics is the most important facet of the work involved. Everything you've listed except "maintenance of the system" is part of statistics (which I'm going to define broadly as: 'what statisticians do'), and system maintenance is only missing in a "keep servers online" sense, not a "make sure that the concept we implemented remains appropriate" sense.
It has always seemed to me just an excuse to run away from the "Artificial Intelligence 2.0" moniker and all the negative connotations that would imply. I dislike the label "Data Science" because there is not really much "Science" with a capital-S being done by people who adopt the moniker and with whom I have had chance to meet.
I have always thought that "Knowledge Engineer" was a more descriptive and useful term for what they actually do. The more abstract you get it seems to fall into the field properly known as Computational Mathematics.