For context, I'm entering a mathematics PhD program that offers several different areas of specialization, mathematical computer science (MCS), pure maths, statistics, and more. My undergrad was in pure maths, so I feel most comfortable there, but I'm also interested in MCS, although I'm essentially a stranger there, other than a course on graph theory and a chapter of Algorithms. I'm trying to decide which track to put myself on while considering job prospects after school.
- Academia, with a notoriously tight job market, but where you get to continue doing enjoyable research, possibly gain tenure and basically continue the freedom from the same "business" demands that industry would have. I'm not sure I have the mettle to make it here, so the next options feel safer.
- Industry, where your skills will probably be underutilized, or even totally irrelevant, meaning you will have to learn entirely new skills -- programming, statistics, etc -- that you're perfectly capable of learning but haven't been directly taught in your PhD studies. These are jobs like actuaries, software engineers, etc.
- Industry Research, rare in between opportunities where research is restricted to what the company needs, but is still relatively open and free. Places like Google and Facebook labs.
There is obvious tension between the first two because a PhD is an academic, not professional, credential. It's simply not optimized to land you into option 2. If you're interested in options 2 or 3, the advice is that you should learn programming or something else on the side and use that to get your first job, and essentially leave the learning in your dissertation behind. This seems like an enormous waste to me. The effort and time spent learning esoteric pure math is essentially for fun (admittedly, a lot of fun), while you are still left to develop marketable job skills on your own.
So my question is: how can I choose my academic track/thesis topic minimize doubling my work? Am I merely butting my head against an intrinsic problem of academic vs professional credential? Should I see the PhD instead as an magical time to enjoy learning math, which will end but leave me mentally strong to tackle other, very different challenges? Perhaps instead it is merely an incubator, where I'm supported while developing a variety of skills not directly involved in the program?
Edit: Thank you for your responses. They're very helpful on a soul searching level and definitely respond to my question, but they weren't exactly what I was looking for.
I should have phrased it like this: What kinds of concrete choices -- courses, advisors, areas of mathematics, etc -- can I make while doing a PhD to maximize its' value after graduating?
For example, is MCS a better choice in terms of applicability outside of academia? Should I consider where a professor's students go after graduating when considering them as an advisor? Am I just worrying too much, and I should just find a problem I love so much that I think about it in the shower? What else am I missing?
Thank you again for your help.