The two opponents enter the ring, each claiming to have the advantage. The data scientist pulls out a silver ruler, the deep learning developer pulls out a shiny hammer: who will build the best model?
In my previous roles, I worked as a data scientist and deep learning algorithm developer. If you ask me what are the differences between the two, I must say that it is it is not clear.
Both deal with data and machine learning models, and both use similar success metrics and working principles.
So what makes them different?
I think it's the attitude.
I'll be bold and generalize that, in my experience, deep learning developers (especially junior ones) tend to focus more on the modelwhile data scientists do the opposite: analyze and manipulate data so almost any model will work.
Or should I dare to simplify it even further and say that:
Deep learning = model-oriented
