Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Mathematicians becoming data scientists

quomodocumque.wordpress.com

Post reply on HN
Print view View original

101–110 of 122 posts

Re: Mathematicians becoming data scientists

#101

Post by hocuspocus »

frozenport wrote: post #100
nerdherfer wrote: post #58

One side of this that bugs the crap out of me: I have a PhD in math and, after getting sick of teaching mediocre students with a palpable aversion to mathematics, I spent a couple of years applying for data science jobs and getting very few responses. The few interviews I got seemed to go well, but I had no offers and gave up on leaving academics. I have experience in software development (C++), I have a portfolio of…

There might be a cost issue. Employers will see the PhD as raising salary expectations without adding value. The only real way to make inroads is to get a job related to your PhD and then do coding work. Typical interview for higher jobs aren't focused on tests or quizzes. If that's what your getting your not applying to the right places - and there might not be many places.

> There might be a cost issue. Employers will see the PhD as raising salary expectations without adding value.

It could be true in some fields (biotech and life sciences, pharma/chemistry, "real" engineering, ...) but in math/CS?

Most companies hire PhD grads at one job grade/level above entry-level, i.e. it's typically not worth much more than a ~2 years head start salary-wise. If a candidate has spent 4/5 years gaining experience in the specific skill set you're hiring for, that's a bargain.

Re: Mathematicians becoming data scientists

#102

Post by ginger_beer_m »

ianai wrote: post #92
nerdherfer wrote: post #58

One side of this that bugs the crap out of me: I have a PhD in math and, after getting sick of teaching mediocre students with a palpable aversion to mathematics, I spent a couple of years applying for data science jobs and getting very few responses. The few interviews I got seemed to go well, but I had no offers and gave up on leaving academics. I have experience in software development (C++), I have a portfolio of…

As a mathematician outside of academia, stay there. Corporate America is not for our kind.

Care to elaborate why? Does the increased pay make up for it?

Re: Mathematicians becoming data scientists

#103

Post by lostboys67 »

olivierva wrote: post #36

Slightly off topic: They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. Imho universities should give a bit more attention on how to write quality software; a bit of test driven d…

Reminds me of my first job at a world leading RnD organisation when I took the test code that the lead engineer on a fluids mixing project had written and added some sensible prompts.

His original Code was cli program who's sole prompt was ?

You had to enter integers , 1, 2 3 etc to select the next option - the possibilities for errors where immense - by this time we had scaled to 1:1 tests where the chemicals for a run could cots over 10K£ per run

Re: Mathematicians becoming data scientists

#104

Post by laichzeit0 »

kriro wrote: post #91
uptownfunk wrote: post #85

I interview probably two to three people a week for a DS job. It's a really difficult role to hire for. I'm basically looking for mathematicians with some knowledge of stats who also enjoy coding and follow best practices in coding (i.e. They didn't just pick it up and hack something that works but care enough to document and structure so others can follow it at a minimum) but we also need the MBA component as well,…

Interesting, thanks. It obviously depends a lot on the job but I would have thought that a good math background (which typically CS grads should have) would be enough for many applications. Especially since you mention the business problem aspect. I think that you can actually get quite far in DS (at least in deep learning) with rather "basic math". Unless you're innovating at the bleeding edge it's usually enough to…

It makes me almost feel sad for the people that followed the "why bother doing a CS degree, you can learn to code without one" trend that probably peaked and is now maybe beginning to die a slow death as the market is flooded with people who can write code.

If they ever want to transition into DS they're going to have to skill up and do the equivalent of undergrad CS math curriculum (discrete, calculus, linear algebra, math stats, etc.), which let's be honest, you cannot pick this up in any meaningful way in a "few months" of after hours/weekend study like you can when learning to program or learning some new "framework"; either that or just remain another dime-a-dozen code-cutter. It's sad because if you just did the 4 years of computer science and all the math and the stats that go with it you'd be so close to pivoting right now if you wanted to.

Re: Mathematicians becoming data scientists

#105

Post by ska »

visarga wrote: post #98
throwaway729 wrote: post #76

Earlier quoted context omitted.

I think you got the parent's meaning exactly backward. The 90/10 thing is about their phd field of study (mathematical logic), and they're saying that something almost opposite of that is true for data science.

I think it's still true for data science. Nobody can keep up with the flood of research taking place.

With data science there is a lot more demand for skilled people who are not at the top of the game. As a result there are a number of really good career paths, which really isn't what you see in academic work.

Industry can cheerfully, usefully absorb thousands of solidly "average" (in this particular sense) mathematicians (or similar) in a way that academia just has no plan for. Even if they do not work on anything quite as technically interesting as they had previously in other ways the job may be more rewarding; And I don't mean simply financially.

That being said, most academics are not a good fit initially.

Re: Mathematicians becoming data scientists

#106

Post by M_Grey »

stale2002 wrote: post #81
M_Grey wrote: post #71

Earlier quoted context omitted.

Oh yeah, you're going to change the world by figuring out who to market baby formula to, and that internet-connected toaster is a winner. The money is good though, that's true.

The money is good because people are willing to pay. If the world needed more PHDs, then the world would pay them more. Thats how markets work. Supply and demand.

"Needs"

Not so much. "Wants" as "Thinks it needs", but even then, the amount of student debt suggests that neither of those are entirely true either.

Re: Mathematicians becoming data scientists

#107

Post by M_Grey »

tempodox wrote: post #93
M_Grey wrote: post #17

Earlier quoted context omitted.

And how often does that pride last once you see the application that work is put to? At least if you do good math, you can take pride in that .

It's just a tool. Don't connect aesthetics or morals with its production.

The problem is that following that, you become a tool as well.

Re: Mathematicians becoming data scientists

#108

Post by xamuel »

As a mathematician who successfully made the leap, I think the biggest requirement is ability to swallow your pride and "lower" yourself to seriously study professional coding.

There's too much temptation to hack something in Python or whatever, google as you go, etc. What I did was sit down and practically memorize entire programming manuals. Many academics would refuse to do something so plebeian.

Re: Mathematicians becoming data scientists

#109

Post by throw_away_777 »

stale2002 wrote: post #82
throw_away_777 wrote: post #79

Earlier quoted context omitted.

This definition of engineer is so broad as to be meaningless. There is no "fake world" with "fake world" problems, pretty much everyone is trying to create real world solutions to real world problems. Similarly most jobs are focused on the end goal. A data scientist job usually involves a mix of science and engineering.

My definition of "real world" problems are ones that people are willing to pay for. If nobody is willing to give you money for whatever it is that you are doing, then it probably isn't solving someone's actual problem.

The problem with this approach is that people pay much more money for limited gains they can exploit than for general gains that benefit everyone equally. Advancing academic knowledge provides gains distributed over a very large number of people, but the current system often does not efficiently allocate resources to this problem.

Re: Mathematicians becoming data scientists

#110

Post by nickdavidhaynes »

throw_away_777 wrote: post #79
stale2002 wrote: post #66

Earlier quoted context omitted.

An engineer is a person who creates real world solutions to real world problems. What matters is not how elegant your solution is, what matters is solving the problem. And doing it under cost and time constraints. An engineer is first and foremost, a person focussed on the end goal, above all else.

This definition of engineer is so broad as to be meaningless. There is no "fake world" with "fake world" problems, pretty much everyone is trying to create real world solutions to real world problems. Similarly most jobs are focused on the end goal. A data scientist job usually involves a mix of science and engineering.

>There is no "fake world" with "fake world" problems

You've never met a number theorist.

Post reply on HN