Advice for stats students on the academic job market12 Sep 2011
Job hunting season is upon us. Openings are already being posted here, here, and here. So you should have your CV, research statement, and web page ready. I highly recommend having a web page. It doesn’t have to be fancy. Here, here, and here are some good ones ranging from simple to a bit over the top. Minimum requirements are a list of publications and a link to a CV. If you have written software, link to that as well.
The earlier you submit the better. Don’t wait for your letters. Keep in mind two things: 1) departments have a limit of how many people they can invite and 2) admissions committee members get tired after reading 200+ CVs.
If you are seeking an academic job your CV should focus on the following: PhD granting institution, advisor (including postdoc advisor if you have one), and papers. Be careful not to drown out these most important features with superflous entries. For papers, Include three sections: 1-published, 2-under review, and 3-under preparation. For 2, include the journal names and if possible have tech reports available on your web page. For 3, be ready to give updates during the interview. If you have papers for which you are co-first author be sure to highlight that fact somehow.
So what are the different types of jobs? Before listing the options I should explain the concept of hard versus soft money. Revenue in academia comes from tuition (in public schools the state kicks in some extra $), external funding (e.g. NIH grants), services (e.g. patient care), and philanthropy (endowment). The money that comes from tuition, services, and philanthropy is referred to as hard money. Every year roughly the same amount is available and the way its split among departments rarely changes. When it does, it’s because your chair has either lost or won a long hard-fought zero-sum battle. Research money comes from NIH, NSF, DoD, etc.. and one has to write grants to raise funding (which pay part or all of your salary). These days about 10% of grant applications are funded, so it is certainly not guaranteed. Although at the school level the law of large numbers kicks in, at the individual level it certainly doesn’t. Note that the break down of revenue varies widely from institution to institution. Liberal arts colleges are almost 100% hard money while research institutes are almost 100% soft money.
So to simplify, your salary will come from teaching (tuition) and research (grants). The percentages will vary depending on the department. Here are four types of jobs:
1) Soft money university positions: examples are Hopkins and Harvard Biostat. A typical breakdown is 75% soft/25% hard. To earn the hard money you will have to teach, but not that much. In my dept we teach 48 classroom hours a year (equivalent to one one-semester class). To earn the soft money you have to write, and eventually get, grants. As a statistician you don’t necessarily have to write your own grants, you can partner up with other scientists that need help. And there are many! Salaries are typically higher in these positions. Stress levels are also higher given the uncertainty of funding. I personally like this as it keeps me motivated, focused, and forces me to work on problems important enough to receive NIH funding.
1a) Some schools of medicine have Biostatistics units that are 100% soft money. One does not have to teach, but, unless you have a joint appointment, you won’t have access to grad students. Still these are tenure track jobs. Although at 100% soft what does tenure mean? The Oncology Biostat division at Hopkins is an example. I should mention at MD Anderson, one only needs to raise 50% of ones salary and the other 50% is earned via service (statistical consulting to the institution). I imagine there are other places like this, as well as institutions that use endowments to provide some hard money.
2) Hard money positions: examples are Berkeley and Stanford Stat. A typical break down is 75% hard/25% soft. You get paid a 9 month salary. If you want to get paid in the summer and pay students, you need a grant. Here you typically teach two classes a semester but many places let you “buy out” of teaching if you can get grants to pay your salary. Some tension exists when chairs decide who teaches the big undergrand courses (lots of grunt work) and who teaches the small seminar classes where you talk about your own work.
3) Research associate positions: examples are jobs in schools of medicine in departments other than Stat/Biostat. These positions are typically 100% soft and are created because someone at the institution has a grant to pay for you. These are usually not tenure track positons and you rarely have to teach. You also have less independence since you have to work on the grant that funds you.
4) Industry: typically 100% hard. There are plenty of for-profit companies where one can have fruitful research careers. AT & T, Google, IBM, Microsoft, and Genentech are all examples of companies with great research groups. Note that S, the language that R is based on, was born in Bell Labs. And one of the co-creators of R now does his research at Genentech. Salaries are typically higher in Industry and cafeteria food can be quite awesome. The drawbacks are no access to students and lack of independence (although not always!).
Update: I reader points out that I forgot:
5) Government jobs: The FDA and NIH are examples of agencies that have research positions. The NCI’s Biometric Research Branch is an example. I would classify these as 100% hard. But it is different than other hard money places in that you have to justify your budget every so often. Service, collaborative, and independent research is expected. A drawback is that you don’t have access to students although you can get joint appointments. At Hopkins we have a couple of NCI researchers with joint appointments.
Ok, that is it for now. Sometime in December we will blog about job interviews.