Art of Message
Recasting the role of the data scientist II
I got an interesting response to this post from friend of the list and actual data scientist (one of several on this list), who also understands how LLMs are designed and built on a nuanced level.
Because his is a more nuanced view than the ones presented in the paper referenced, I’m sharing parts of it, with his permission. Don your nerd glasses now, as we plunge you deep into the future nexus of generative AI and enterprise data science.
John responds:
“I think the enterprise data scientist will be transitioned to product manager, one who … will also become more embedded with marketing, as well as data visualization and first-cut analysis.
But I also think the enterprise will still require, especially in large orgs, professional data scientists. I’ve seen this first hand with a multi-billion dollar grocery chain. They found so much value sitting on the floor of their mainframe, that it required a hardcore data scientist to go extract it.
…Even training data-science-focused LLMs – just operating them you’ll need a scientist. But to handle, manage, organize, mine, store properly, scale, and so many other things, the ever growing amount of data enterprises will have, you’ll _require_ a full scientist. “
What I get out of this is that even if some in a data science role take on responsibilities that look more like product management, the demand for data scientists will still be there.
Makes sense if you consider what it entails: statistical analysis, pattern recognition, inferential and predictive modeling, data transformation, pipelines, and architectures – plus the oversight of custom-built LLMs that help with all of the above. Also: domain expertise and the ability to communicate insight.
The generalist finally has the upper hand in the generative AI era – but the data scientist was a generalist to begin with.
