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Sweet, according to his list I'm over-qualified. Interesting to think it would be so easy to make the transition to data science. Except I can't imagine wanting

• scott_s 13y ago
  Not everyone has the luxury of working in the field they got a PhD in. Two of my friends were looking into switching to data science positions because they were having problems finding positions in their fields. One was on her second post-doc as an astrophysicist, and the other is a soon-to-gradute biochemist. Both did find positions in their fields, but not before much fear and existential angst.
  • Blahah 13y ago
    I can certainly see how the harsh realities of the job market might force one's hand. It sounds like your friends' priorities were the same as mine though...
    • scott_s 13y ago
      Actually, no. One of them actually preferred a data science position, and was scheduled to interview for this very program. But that person's spouse was not okay with moving to California, so that option was out, and a post-doc position came through.
• besquared 13y ago
  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.
  • tmarthal 13y ago
    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 wall-street in the 80's. You have to be in it to make money, rather than caring about the types of data the developed algorithms are applied on.
    • dev1n 13y ago
      I mean, developing a new algorithm for data analysis could be just as important to the OP as analyzing bio data to forecast some sort of disease is to someone else.
    • rogerchucker 13y ago
      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.
  • Blahah 13y ago
    I doubt that. Firstly because most people don't hate anything in academia, and secondly because you know very little about me. Perhaps my short comment sounded more arrogant than I am... I'm motivated by wanting to help people. If given the choice between trying to help alleviate starvation for little money and trying to optimise advertising on some website for a shitload of money, I'll take the former. Interesting that you think moral judgements are an indicator of self importance.
    • burntsushi 13y ago
      > Interesting that you think moral judgements are an indicator of self importance. Definitely not. But the way one expresses them certainly is.
• acadien 13y ago
  Ive got strong convictions too and the big data fad (real or not its still a fad) seems specious and unfulfilling. But as a PhD candidate who has had his funding cut and grant proposals continuously denied since sequestration, money of any kind is starting to sound good right now.
  • Blahah 13y ago
    true - I might be whistling a different tune if the money runs out
  • deleted 13y ago
    [deleted]
• rdouble 13y ago
  calling all data scientists - why not consider becoming a computational biologist? The pay is shit, you're at the whim of the funding moods of the day, and contrary to your last statement most of the results don't really ever go on to affect anyone.
  • Blahah 13y ago
    I agree about the pay. It really sucks. But the other things depend what you work on and where. If it's cancer or food security, the funding is there and not going away. And you can choose how direct the outcomes are by choosing the position. I'm not saying everyone should do it, but if you're good enough to breeze into highly paid positions at top tech companies, you're good enough to get a really interesting position in computational biology.
    • rdouble 13y ago
      Yeah, it can be interesting but often times it's almost the same sort of thing as any lousy computing job on a day to day basis, it just pays worse, with weird academic attitudes and bureaucracy tacked on. Plus, when I did it you were stuck using some janky Perl scripts and whatever bogus Java package was promising to replace the perl scripts of the day. I worked in a lab at HMS that sounded interesting on paper but wasn't all that interesting in practice. The researchers did the same stuff as you would at The Office, they checked ESPN.com, went to meetings, typed in some SQL and Perl codes for a while, went on coffee break, complain about something, go to another meeting, fart around with the design of their conference poster, etc ad nauseum. Then they all just went to go work at some big corporation, anyway. The upside was low expectations so I was able to work almost full time as a contractor on something interesting at the same time.
      • Blahah 13y ago
        That sounds tedious, but it's not my experience. People do leave to work at big corporations because the stress and relatively low pay of academia drives them to it. But the work here is pretty exciting.
• rgovind 13y ago
  What skills do we need to learn to get into such a position? That is apart from statistics and programming? How much effort will go into learning that stuff?
  • Blahah 13y ago
    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 the major high throughput biological technologies and the kinds of data they produce, along with the current tools used for processing the data You could pick up all of that in a year of intense self-study, and less assuming you already have some of those skills.
    • advice4u 13y ago
      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)?
      • petegrif 13y ago
        A lot of statistics. It's core.
        • njbooher 13y ago
          It's unfortunate that biological statisticians have hijacked the term 'computational biology'. There's still a lot of computer science to be done in the area, particularly in genome assembly what with new sequencing technologies appearing every few years.
          • 13y ago
• daemonk 13y ago
  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 academics take it while constantly assuring themselves that "it's important and they love it". I am sorry that I am coming off as extremely cynical, but I really don't think propagating the idea that pursuing pure science is somehow more virtuous than other professions helps with the situation. And in my opinion, as inexperienced as it might be compared to more established scientists, computational biologists are ready for biology, but biologists are not ready for computational biology.
  • _delirium 13y ago
    > 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, especially if you're in a hot area like bioinformatics, have made a name for yourself, and can get a position at a top-30ish place. Unlike in tech, those salaries often come in places with a lower cost of living than SF, too (at least if you want them to). Six figures goes pretty far in Atlanta, Austin, Urbana-Champaign, Ames, or Raleigh, for example. You could beat that in industry, but either way you're making solidly in the top 10% of U.S. salaries. And if you really need more money, most universities will let you do 20% consulting time, or do a spinoff startup. There are admittedly other reasons not to go into science academia (the list is pretty long, actually), but fear that you'll have to take a vow of poverty doesn't seem like a strong one.
    • suchire 13y ago
      Postdocs in biological/physical sciences are easily 3-6 years for < $60k (generally closer to $40-50k in the past 5 years), and the chances of getting an academic position with meaningful salary are very low. Postdocs are plentiful, positions are scarce. Meanwhile the postdoc could have been making $100-120k for a profession where the job market is almost the polar opposite, which makes a difference for job security/stress. The 2x-4x salary difference, especially during the late 20s and early 30s, is a pretty big deal, especially if you're able to save that extra 2x-4x. The even crappier pay in humanities makes it basically undoable except for the upper class or outsizedly talented. The low pay in science makes it doable for normal people, but there is a strong financial incentive not to.
• dev1n 13y ago
  Maybe you could help me out here. I'm split on the whole data science in research vs business. I'm an undergrad Senior majoring in CS (minors in math and Computational Science). All the data science jobs I see for research firms require a PhD. I'd much rather work for a research firm than as an analyst for a business (I think). Any suggestions for someone looking to get some experience before pursuing more schooling? (not like I don't enjoy my classes but I'd rather not drop the dough after undergrad if I can get decent experience and a salary to help pay for a graduate program)
  • Blahah 13y ago
    Depending on where you are in the world, you could consider applying for computational jobs at some of the biotech giants (or startups, depending on your cultural preference). There are plenty on the US west coast and around Boston, Seattle, etc. An alternative is to get a programming job doing something relevant (e.g. something with applied machine learning) and use those skills to work on open-source bio projects in your spare time. You'd then have some money, relevant experience, and demonstrated interest which could be a good foundation for graduate work if you decided to go that route, or for a career in data science if you don't.
    • dev1n 13y ago
      Cool. Thanks for the advice. My game plan was pretty much just that. Get a solid programming job doing (preferably) some stats work after graduation (maybe the company will pay for grad school?) and then move on from there. I just wanted to make sure there wasn't one weird trick to landing a research job in data science.
  • singingfish 13y ago
    Most jobs in high tech that push on the fronteirs of scientific knowledge that have a group leadership role need a PhD in charge of the team. Back when I worked in biotech, around 3/4 to 5/6ths of the team leaders had PhDs. I wouldn't expect this to have changed. For a commercially viable biotech company, salaries are pretty decent at that level.
• gammarator 13y ago
  The majority of PhDs will not obtain permanent careers in science. In the UK, it's less than 4% (0.45% professors) [1]. Most of your peers--and perhaps even you--will find themselves searching for careers in a new field at some point. Let's not badmouth them for taking a good opportunity. [1] Figure 1.6 of http://royalsociety.org/uploadedFiles/Royal_Society_Content/policy/publications/2010/4294970126.pdf http://royalsociety.org/uploadedFiles/Royal_Society_Content/...
  • Blahah 13y ago
    I don't think I badmouthed them, I just said I can't imagine wanting that. Being forced into it is another matter. I totally sympathise with anyone who is forced out of science due to lack of jobs.
• ksolanki 13y ago
  Calling all data scientists -- why not consider working on medical and/or biological data instead? There is not necessarily less money in these fields, but a much much greater potential impact!
• jtmcmc 13y ago
  really messy data with political silos surrounding access to it and often a really shitty sample size:feature space size ratio. Not to mention frustration surrounding funding for primary data generators and then all the other problems related to the extremely competitive world of academia.