A recurrent question that I’ve gotten over the past days is “Why changing from Data Science to Machine Learning and back (so often)?” 🤔
First of all, don’t worry I understand the confusion it happened to me as well 😅
The answer is very simple tho: the most valuable lesson I’ve learned over the past years is that when looking for an ML-related position is better to research about the industry in which the company operates than the title itself. ALSO most of the times the job offer does not really reflect your daily tasks, and you can get really surprised (and disappointed) after joining something you had high expectations of.
As an example, I missed so many Computer Vision opportunities when I graduated as most of the positions were covered by a pure Software Engineering - C++ title. To my surprise, I found out later on that those companies were actually working with very cool technology such as autonomous driving.
Most companies are very much in a similar position as a couple of years ago, they don’t know whether to call it SWE, DS, AI, ML... and since there seems to not be a consensus yet, the most important task for you is to ask good questions when going to an interview.
And that is a fairly simple explanation of why my titles just vary so much. But in reality, I am still doing what I love, and that is the important part of all of it. Not the title!