From PhD to Data Scientist: Tips for Making the Transition • Hacker News Board

Re: From PhD to Data Scientist: Tips for Making the Transition

#61

Post by mbreese » Thu, Aug 01, 2013, 12:56 AM UTC

jurassic wrote: post #57

_delirium wrote: post #40

Earlier quoted context omitted.

> shitty pay I can see this complaint in humanities academia, but pay in the sciences past the PhD student level is pretty reasonable. You could probably make more elsewhere, but it's not like you're scraping by on ramen noodles as a bioinformatics professor or anything. Postdocs typically make $50-60k, and professors start at something like $90k at the minimum, easily up to $120k, $150k, or more after tenure, especi…

This response tells me you've never been in the science trenches. I've never heard of a postdoc making anywhere close to 60k, even at elite schools in high COL areas. The financial opportunity cost is staggering.

Depends on what you do...

Straight wetlab postdocs are usually around the NIH levels (~40K). For computational postdocs (especially if you have a good biology background), 50-60K isn't out of the norm.

Re: From PhD to Data Scientist: Tips for Making the Transition

#62

Post by mbreese » Thu, Aug 01, 2013, 12:56 AM UTC

scott_s wrote: post #47

_delirium wrote: post #40

Earlier quoted context omitted.

> shitty pay I can see this complaint in humanities academia, but pay in the sciences past the PhD student level is pretty reasonable. You could probably make more elsewhere, but it's not like you're scraping by on ramen noodles as a bioinformatics professor or anything. Postdocs typically make $50-60k, and professors start at something like $90k at the minimum, easily up to $120k, $150k, or more after tenure, especi…

My biochem friend just accepted a post-doc at a respected lab for $39k.

For just wet-lab people, this is still a little low, but not by much.

Re: From PhD to Data Scientist: Tips for Making the Transition

#63

Post by _delirium » Thu, Aug 01, 2013, 1:06 AM UTC

rdouble wrote: post #60

_delirium wrote: post #59

Earlier quoted context omitted.

Well, you'd be incorrect, since I'm currently a science academic. Have you checked what Stanford, or Georgia Tech, or UT-Austin pay postdocs with machine-learning or data-mining experience, in the past 5 years? There are definitely areas that pay less, but bioinformatics, if by that you mean people with serious computational skills, pays above the norm.

Getting a postdoc at Stanford has the same probability as playing in the NBA. Only you get paid $60K instead of $60M.

If you have strong machine-learning experience and a few good publications in the current market, getting a postdoc at a top institution is nowhere near NBA odds. I don't know what the odds are specifically for Stanford, but if you apply to whoever has openings among top schools, there are many each year. If you know something about biology and a lot about machine learning, labs might even recruit you rather than vice versa.

Re: From PhD to Data Scientist: Tips for Making the Transition

#64

Post by tomrod » Thu, Aug 01, 2013, 1:21 AM UTC

aet wrote: post #48

tomrod wrote: post #2

I'm currently finishing a PhD in economics and have spent a lot of time learning the exact technologies he suggests (Python, SQL, a bit of R). Working as a data scientist would be an awesome opportunity. But are most companies _really_ in need of so many data scientists, or is it just a trend?

Have you already started into your specialty? Maybe you should do econometrics.

I'm far into my fields. I do econometrics, computational economics, and industrial organization.

Re: From PhD to Data Scientist: Tips for Making the Transition

#65

Post by rogerchucker » Thu, Aug 01, 2013, 1:27 AM UTC

tmarthal wrote: post #17

besquared wrote: post #8

Earlier quoted context omitted.

You're everything that everyone hates in academia. Congratulations as I hear that self importance is one of the key ingredients to solving the biggest problems facing the world today.

Well, I would posit that the majority of data science positions are in advertising and marketing optimization. I would believe that 'global food security' may be more important than social media analysis, as the original posted speculated. Not to belittle the meaningful point, but becoming a "data scientist" at a startup or large corporation that makes their money by advertising is analogous to becoming a 'quant' on…

Actually advertising and marketing optimization is a very critical problem that hasn't been completely solved yet, especially when privacy concerns are taken into consideration. It is critical towards keeping the Web free and create a more inter-connected economy. While this will obviously have the side-effects of "frivolous" analytics, there is indeed a dearth of enough hybrid practitioners-researchers in data science today. It will probably saturate in 5-10 years but I'm no analyst to predict that.

Re: From PhD to Data Scientist: Tips for Making the Transition

#66

Post by throwaway1979 » Thu, Aug 01, 2013, 1:45 AM UTC

_delirium wrote: post #63

rdouble wrote: post #60

Earlier quoted context omitted.

Getting a postdoc at Stanford has the same probability as playing in the NBA. Only you get paid $60K instead of $60M.

If you have strong machine-learning experience and a few good publications in the current market, getting a postdoc at a top institution is nowhere near NBA odds. I don't know what the odds are specifically for Stanford, but if you apply to whoever has openings among top schools, there are many each year. If you know something about biology and a lot about machine learning, labs might even recruit you rather than vic…

Also, finishing a post-doc from a top lab does not mean you will get a professor job at a decent school.

Re: From PhD to Data Scientist: Tips for Making the Transition

#67

Post by kylebrown » Thu, Aug 01, 2013, 1:50 AM UTC

Blahah wrote: post #44

daemonk wrote: post #37

Earlier quoted context omitted.

I am graduating phd bioinformatician, most likely going to transition into industry. It's very easy to be caught up with the self importance of academia because you are essentially in a bubble. It's great to be passionate about science, but I really dislike religifying academia. It's almost expected of aspiring academics to live like monks and just to be okay with shitty pay and long hours. That's bullshit and academ…

There is definitely an academic bubble. The infamous ivory tower. But in biology specifically, many of the problems are objectively important (as judged by society). And some people really do love spending all their waking hours working on them, and don't give a crap about the money. I wouldn't call it virtuous, but it is deeply intellectually satisfying. Agree about biologists not being ready - computational biology…

One bubble which could use piercing is the hard science one (please, humor me).

Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so growing more food won't necessarily help).

IMHO, the important problems in the world are much more social and political, than technological. The work twitter does may easily have greater beneficial impact, direct or indirect (Arab spring and all that), on global food security than working for Monsanto on GE crops. I wouldn't be so self-righteous for choosing to work on "hard" science problems. Are you really doing it for the benefit of the world, or just for the deep satisfaction of your own curiosity?

Re: From PhD to Data Scientist: Tips for Making the Transition

#68

Post by petegrif » Thu, Aug 01, 2013, 1:53 AM UTC

advice4u wrote: post #54

Blahah wrote: post #38

Earlier quoted context omitted.

If you had most of these things I think you would have a good shot at a compbio position: - statistics, probability, and especially probabilistic inference - nix/gnutools - multiple scripting languages (Ruby, Python, Perl, BASH) - at least one data-oriented language (R, Octave) - understanding of molecular biology (read Molecular Biology of the Cell) - applying machine learning tools to new problems - understanding t…

This is something I would really love to do. Where do you work (in academia, I presume)? I have the programming background, and a bit of the bio background... but I am weak on statistics. How much of statistics and probability theory would I need (beyond a basic 1st-year college level)?

A lot of statistics. It's core.

Re: From PhD to Data Scientist: Tips for Making the Transition

#69

Post by aspivelox » Thu, Aug 01, 2013, 1:53 AM UTC

_delirium wrote: post #63

rdouble wrote: post #60

Earlier quoted context omitted.

Getting a postdoc at Stanford has the same probability as playing in the NBA. Only you get paid $60K instead of $60M.

If you have strong machine-learning experience and a few good publications in the current market, getting a postdoc at a top institution is nowhere near NBA odds. I don't know what the odds are specifically for Stanford, but if you apply to whoever has openings among top schools, there are many each year. If you know something about biology and a lot about machine learning, labs might even recruit you rather than vic…

This has certainly been my experience as a recent PhD in computational biology. I pretty much have my pick of Post Doc positions - I was getting offers before I even graduated. I was also able to negotiate 50k without much trouble, but I'm definitely worried about the opportunity cost. Giving up 100+ k for more than a year or two seems like a poor decision.

Also a Post Doc from a top lab directly correlates with how much $$ you can make in industry.

Re: From PhD to Data Scientist: Tips for Making the Transition

#70

Post by petegrif » Thu, Aug 01, 2013, 1:55 AM UTC

tomrod wrote: post #2

I'm currently finishing a PhD in economics and have spent a lot of time learning the exact technologies he suggests (Python, SQL, a bit of R). Working as a data scientist would be an awesome opportunity. But are most companies _really_ in need of so many data scientists, or is it just a trend?

It is a trend. The question is rather, is it a trend that is likely to persist? And that depends on whether you believe that organizations are likely to capture and store more data or less. If you believe the answer is 'more' then the problem becomes deriving insights from it. And that process - is data science.