The above-linked post is fantastic. Here’s the authors intro, strongly recommended if this resonates with you:
Recently, I came across a Reddit thread
on the different roles in data science and machine learning, such as data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc.
I found this worrying. It’s difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view.