Like Berton Gunter said, jobs are usually classified by subject than softwares used. It is difficult to change the mindset of people in a workplace that worships software A and condemns software B. Try learning enough of A to know its weakness/strengths and demonstrate some examples where B can do the job much better than A. Warning : This can be a slow and sometimes a pointless one.
What you should be looking for instead is for a flexible and understanding employer that will allow you to experiment with other softwares. You could enquire about this before you apply for a given job. My biased opinion is that academic line gives you this flexibility. If you are interested in academia in UK, check out www.jobs.ac.uk. As for bio/pharamaceutical-related jobs, especially those dealing with *omics technology, knowledge of R and BioConductor can be a real advantage. Some of these are advertised on the BioConductor mail list. Regards, Adai On Mon, 2005-08-29 at 11:04 -0500, Weiwei Shi wrote: > Hi, there: > Could I ask another question, which is a little bit off-topic; but I > tried hard and did not get good enough info... so please help > > I am very interested in seeing where to find those > bio/pharmaceutical-related industries, using R and data mining as > approaches? > > thank you very much! > > weiwei > > On 8/29/05, Berton Gunter <[EMAIL PROTECTED]> wrote: > > Avneet: > > Not to throw a wet blanket on your enthusiam for R (which I share) but ... > > > > -- Bert Gunter > > Genentech Non-Clinical Statistics > > South San Francisco, CA > > > > "The business of the statistician is to catalyze the scientific learning > > process." - George E. P. Box > > > > > > Your better off finding a > > > job you like > > > at a company you like and then convincing them that R is > > > better (not to > > > mention the R skill set you are bringing to the table). > > > Good luck to you. > > > Roger > > > > Fine advice, but a tad unrealistic. The reality (according to Bert): > > > > 1. Most jobs for statisticians are in the pharmaceutical/medical industry > > (which includes academic research centers) in clinical trials. Data: See job > > ads in Amstat News. > > > > 2. For better or worse, in this arena SAS is the standard. You will **not** > > -- repeat, NOT -- convince industrial employers who have thousands of lines > > of legacy infrastructure code and legions of SAS programmers to change. You > > may well make some inroads in academic research venues. In both, you will > > generally be free to use whatever software you like for your own work, but > > the final code submitted for FDA approval will almost certainly necessarily > > be SAS. Rail all you like, but those are the realities. > > > > 3. Another significant amployer of statisticians these days is the "finance" > > industry (credit scoring and the like). Data: See Amstat News ads again. > > There S-Plus is already widely used, so you should have no difficulty using > > R and even getting others to adopt it. > > > > I think outside these arenas -- for example, in industrial research and > > engineering centers or in pre/non-clinical pharmaceutical work, you'll again > > be free to use what you like. But there are relatively few jobs there, so > > that despite Roger's noble advice (with which I again agree), first you > > gotta eat and pay the mortgage. > > > > And I also say: good luck. > > > > -- Bert > > > > -- Bert Gunter > > Genentech Non-Clinical Statistics > > South San Francisco, CA > > > > "The business of the statistician is to catalyze the scientific learning > > process." - George E. P. Box > > > > ______________________________________________ > > [email protected] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > > > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
