[p4wnc6]

FWIW I am basically in the same situation. My base pay is in the same region you describe for yours, and I rarely see offers that are even 80% of what my salary expectation is, let alone other forms of compensation and other benefits.

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.

[DINKDINK]

> I rarely see offers that are even 80% of what my salary expectation is

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.

[JoshDoody]

This seems very, very plausible to me. Jobs in this experience level and pay range aren't generally posted publicly, and don't stay posted very long if they do go public. This reminds of the same sort of phenomenon with "the good houses"—they never make it to market because someone knows someone knows someone who wants a house like that, so they get first shot at it.

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").

[p4wnc6]

I think this is a major factor, but it has still happened to me even when interviewing for boutique jobs via a friend's reference or happening to know someone who already worked there.

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.

[vidarh]

A substantial proportion of jobs never even makes it onto job boards. And yes, higher paying jobs are often amongst them. I've been approached for jobs that have never been listed publicly several times. It's one of the reasons why it can pay to be on good terms with (the right type of) recruiters and (more importantly) have a good network of industry contacts.

[Mickchicken2]

Sorry this is off topic.

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.

[disgruntledphd2]

If you know almost nothing about the field, then introduction to statistical learning is a good choice.

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 :)

[Mickchicken2]

Thank you for this, i really appreciate you sharing these resources.

[guftagu]

I'm on that coursera course too! The course is pretty basic though. It'll help you get the concepts but there's too much spoon feeding in there to make you good enough to compete with people with MS and PhDs. Also that course doesn't cover deep learning and you should definitely study that.

[Joeri]

I don't know how reproducible the approach is, but i'm working my way in from being a php developer previously. The company i work for is building a big data / machine learning platform from the ground up, and they bootstrapped the project from existing employees, including myself.