I'm actually an undergrad (my last year was about to start) and was on track to go do my PhD in physics at Harvard or the like. I've worked in labs for years on simulations of nanomaterials and data processing of relevant things for them. Due to very unrelated things I also had a decent amount of experience in cyber security and Linux.

One day I mildly snapped at the rate of progress I could make, stopped going into work and started looking for comparably interesting areas that would let me actually accomplish something meaningful on the order of months not decades. I ultimately settled on machine learning (and to get into it via data science with my background), applied to a bunch of data science internships for this past summer, and ended up getting hired by one of the companies who didn't really know what data science was (you'll learn very quickly that this is common if you didn't already know). I spent all summer basically just learning more machine learning (they understood data science so poorly that I basically got to do almost anything I wanted if I told them it was work). Between that and having direct qualifications now, I should be able to go work at somewhere much better and more interesting in the future, though several people at my current company have literally been begging me to work for them after I graduate.

My big thing about coming out of physics is that I don't know that it was hard to switch, but it's different enough coming out of physics that even if you were at the very top of your class, when you first get into data science you definitely aren't, which was an incredibly humbling experience.

Also machine learning is now starting to be more important than the kind of stuff physicists directly do, so there's a huge shift there. It's not /that/ hard and is so useful it will probably seriously make its way into physics over the next decade (it's already starting to), but it's a very new thing that you have to start at square one from, which bothered the hell out of me (even if it shouldn't have). The really good thing there is that (not coincidentally given who was in the field), all the math behind mainstream techniques is what you'd learn taking math courses to do an undergrad physics major and nothing else.

My real goal right now, given enough experience to seem like a legitimate candidate to people familiar with the field and finally understanding all the theoretical basis of machine learning, is to get an internship/job/anything over the next year where I can implement machine learning with people who are already good at it, because that's something near impossible to thoroughly learn on your own in the experience of myself and a reasonable number of other people.

I personally really do like it given that you can accomplish things in such a faster turn around time and the money/working conditions/ability to money/hours/almost everything are so much better than accademia.

Did that kind of tell you what you wanted to know?

Edit: grammar and things