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Comment by agentofoblivion | Hacker News Reader

That hasn’t been my experience. I have M.S. in physics at a shitty school and went into industry. (I didn’t fail anything, and actually had a 4.0 gpa, just didn’t want to spend more time in school). Eventually switched from material science to data science. Currently an ML research scientist at AWS. It’s been a fun journey. No one gave a shit about what I did or didn’t do in college. It might matter to HR people, which can be the resume gate keepers. But the hiring managers and peers don’t care. In fact, if you polled them, I would guess half would know my background is in physics, a third that I have a masters, and 0 which school I went to.

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I think you're covered by the parent's "besides learning to code" caveat. That's a real smooth career transition, I did it as well as a ton of friends who had the same background. ML is a particularly good fit because physics majors just LOL at the "difficult" math that trips everyone else up.

I feel like any bachelors degree in the sciences should have you capable of doing any math needed.

I am quite concerned that Doctors in the medical field have limited to no understanding of statistics. IMO, stats should be second nature to anyone with 200 level math courses.

I'm not sure how 200 level math courses prepare you to review anything more than the most basic experiment designs.

Because 99% of math is basic calculations.

0.9% are 100-200 level math problems.

0.1% are beyond that, but at that point, hopefully your 200 level skills have taught you enough to learn about solving that.

In my lifetime, I only had 1, beyond 200 level problem that required research on math to understand. And technically, it was optional, but I volunteered.

Everything else was algebra.

Stats is theory + math not just math. Maybe not even any math at all, just light programming using stats libraries. If you apply them wrong though, that's a big problem. Biological experiment design is a grad course, so 500 level. Still I agree that a doctor understand the theory.

This might be overstating it a bit. Just this week I've been studying the AdaNet paper and grappling with Noise Contrastive Estimation. I agree that with a B.S. in physics you'll at least get the calculus you need, but I think a graduate level degree really deepens your understanding since it's not the first time through and you're already familiar with the basic concepts.

Regarding doctors and stats, I share your concern. But I disagree with the statement that stats should be second nature to anyone that's gone through the courses. If I've learned one thing while going deeper and deeper into stats, it's that there's a lot more nuance than I originally understood, and I'm still not there. Just when I think I have a thorough understanding of p-values and the like, I'll read some "I can't believe everyone doesn't understand THIS" blog and see that there was more to the story still again. It's hard to know what you don't know.

I'm currently getting a Masters in CS (specialising in ML) after leaving a pure math PhD. I'd love a research type job from anywhere, let alone Amazon, but every job posting for roles with 'research' in the title says they require a PhD. Was your success a case of simply applying anyway? I know they say to apply anyway but it feels like a long shot to me. Do many of your peers have a similar background to you?

When they say they want a PhD, what they're really saying is that they want someone with a proven track record of generating and finishing their own researching projects, publishing top-tier papers, and who knows the state of the art in the field. Because you can't graduate from a PhD without that.

But if you happen to have those things without a PhD, then do apply anyway and I'm pretty sure they'll be interested.

If you don't have those things, then you may not be ready for these kinds of jobs.

There are several very successful academic and industrial CS researchers without PhDs - Simon Peyton Jones for example - so it can be done.

My primary mechanism was reaching out to recruiters and people on LinkedIn that already had the jobs I wanted. I found that just applying through the job site almost never went anywhere. It comes down to a numbers game, so I would sort your hypothetical list of jobs from "dream job" at the top to "I guess" at the bottom, and then start applying from top down. You should absolutely take job postings and required skills with a healthy grain of salt. These are almost always generic, copy/paste artifacts.

An exception might be if you see "Publications in top-tiered journals" as a requirement. Those positions likely actually target PhDs with relevant publications. They might overlook a lack of a PhD if you're a recognized expert in the field, of course. But I interpret this requirement as "we want an academic", and there primary job is likely research and publishing papers. A Research Scientist at AWS is not an academic, as someone at Google Brain might be, but rather someone building applied ML solutions.

Would be interested to hear what you do and don’t like about your job and what skills you learned from your degree

I can’t actually think of anything I don’t like about my current job. The main thing I’ve learned from physics is being comfortable with mathematics and the confidence to work through tough technical stuff that I don’t understand. I think the math basics and grit can take you a long way.

Any tips for getting into Amazon's research arm? I'm in the market, as it were.

Research Scientists are mainly just data scientist in that we’re building models for the business, not just for papers. But we do try to push the boundary and contribute academically. The Data Scientist job family is similar, but lower technical bar and heavier on SQL/basic analysis.

To get into RS position, just know your ML/stats basics well and be proficient in SQL. Nothing super hard about getting an RS role.

Gotcha, thanks.

That's close to what I was about to post as well. In fact I believe physics grads would also be a decent fit in HFT either as quants or SWE dealing with quants.

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