I agree with you take the there isn’t a lot of specialist work for data scientists to do with using off-the-shelf LLMs that can’t be done by an engineer. As an AI-aware software engineer myself… this stuff wasn’t that hard to pick up. Even a lot of the work on the Evals side (creating an LLM judge etc.) isn’t that hard and doesn’t require serious ML or stats.

But aren’t there still plenty of opportunities for building ML models beyond LLMs, albeit a bit less sexy now? It’s not like you can run a business process like (say) AirBnB’s search rankings or Uber’s driver marching algorithms on an LLM; you need to build a custom model for that. Or am I missing something here? Or is that point that those opportunities are still there, but the pond has shrunk because so much new work is now LLM-related? I buy that.

> I agree with you take the there isn’t a lot of specialist work for data scientists to do with using off-the-shelf LLMs that can’t be done by an engineer.

Conversely, data scientists are doing software engineering, including webdev. It’s an interesting time. I think it’s less about the job title demarcation now, and more about output.