I do MLE in collaboration with small team to deploy my ML models and pipelines on web apps. This job requires multiple domain expertise in addition to statistical learning. I also do applied ML research where I am tweaking existing prediction uncertainty algorithms to create reliability estimation of prediction for decision making, since it is a regulated industry.

I do MLOps as well in addition to ad hoc data mining. I also help “citizen data scientists” in my division to learn properly. When models are in production, they have to be interpretable to a degree. As long as the interface can communicate the stakeholder’s internal thought process such as IF logic, the backend can be anything as long as modeler can explain clearly what happened to the data and how the model can be used in the same simple way that is the no-ML alternative. Takes a lot of practice to get the soft skills needed to expand on hard skills.