I'm suspicious that this is a silent-evidence phenomenon. I'd assume that the duration a job listing is up correlates inversely proportional to the compensation. If that's true, all the great paying jobs are optically invisible.
These sorts of jobs are best found through one's network. Not only does this open up better opportunities, but this sort of "back channel" opportunity usually comes with more details than a public job listing (such as the "I think they're probably paying $YYYk or so").
A fair amount of the interviews I accept come from unsolicited head hunters seeking me out, and even then the reveal-your-salary thing is a huge sticking point. And even when it hasn't been, I've still been let down by the ultimate salary offers.
I heard once that you should budget 1 month of job searching for every $10,000 of compensation you seek, and maybe more once you are seeking high-level bonuses. That's easily been true in my case. You also have to put up with horrid HR people who are quite simply just rude to you, and lots of degrading "dance monkey dance" whiteboard and coding puzzles and hazing, and demoralizing cross country interview trips that you have to muster the ability to put your whole self into but which you cynically know in your heart of hearts that it's a waste of time.
Is there a way into machine learning without doing going and going an Ms or PhD?
Im working my way through a coursera course on it but I'm not sure that is enough. All the positions I see are looking for academic experience or 5+ years doing it. Neither of which are doable for me.
http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p...
It assumes some understanding of calculus, but doesn't require matrix algebra.
The original (and amazing) book that lots of people used is Elements of Statistical Learning.
https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI...
Chapters 1-7 are worth their weight in gold. This is one of the cases where the physical books are much better, as you'll need to flick back and forth to see the figures (which are one of the best parts).
The forgoing assumes that you already know some statistics/data analysis (the latter probably being more important).
If you haven't done this before, then I suggest that you acquire some data you care about, install R (a good book is the Art of R Programming by Matloff), and start trying to make inferences. And draw graphs. Many, many, many graphs.
If you keep at this, finding papers/books and reading theory, and implementing it in your spare time, then you can probably get a good data science job in 1-2 years. You'll probably need to devote much of your free time to it though.
I'm assuming that you can already code, given the context :)
I am in scientific computing and machine learning, and I actually love being an implementer and a coder. I don't want to be a people manager (mostly because I think all companies force managers to treat their subordinates in unhealthy ways, and no matter how good your intentions are, being a manager will ruin your humanity).
I worry about finding any job, and then further worry about what growth path there could possibly be.