Career options for an epidemiology PhD?

[Kaylee]

My background:
I am a 3rd-year PhD student in epidemiology, with a strong interest in genetic epidemiology. I am also a lifelong computer tinkerer, but have never worked as a professional software developer. I've taken a few masters-level bioinformatics courses during my PhD just out of interest/to scratch the coding/tinkering itch, but it is not my PhD area.

Going into graduate school, I thought my career path was obvious: tenure-track professor. But now that I've seen what life is like for them -- chronically busy and overworked, and most of their time on teaching/service/administration not leaving much time for research work -- I am seriously rethinking my desire to pursue that career path. But at this point I have only a vague idea what the alternatives are...

I like:
1) Research to find out what causes diseases, especially from a genetic epidemiology perspective
2) Writing code
3) Analyzing data

I don't like:
1) Highly unstable jobs where I would be constantly, frantically writing grants and lying awake at night worrying about losing my job if they don't come through (I think most non-tenure-track positions like "research professor" are like this?)
2) Being in a "management-level position" or other jobs that would involve not being able to get my hands dirty with the things on the "I like" list
3) The thought of leaving science and doing something like working with data for marketing/insurance/etc.

What career options (in or out of academia) are available to me if I still want to be able to spend at least 25% of my time on my own research topics (there is a specific disease I am particularly interested in working on)? Are there positions where you can try for your own grants, but don't have to worry too much about the times when you don't get them because then you just spend more time working on other peoples' projects instead of losing your job?

Or, if I am willing to let go of the idea of choosing my own research topics, what PhD career paths out there would maximize the amount of time I get to spend on things on the "I like" list while minimizing/avoiding the things from the "I don't like" list?

[Dave Walker]

Hi Kaylee, and welcome to our forum! Thanks for posting your question. I think we've all been there at your "career discovery" stage at some point :)

My specific answers to your question are below, but I recommend doing some homework first -- some "informational interviewing" (we have an article about exactly this: http://www.sciencemag.org/careers/2009/03/informational-interview). Where do the graduates from your PhD program go? Do you know any successful epidemiology PhD students who have a career you would like to emulate? Talk to them, and ask how they did it -- I guarantee you'll get some great advice.

Also, I think you are forming biases that are not necessarily helpful. Do all epidemiology tenure-track professors in the world stay up at night worried with grants? Instead of focusing on the worst-case scenario, think about the job duties, and ask yourself if you like them. Have you written a grant yet, or helped a professor with one? It is a great way to see if you would like doing that.

Putting these ideas together: have you met any professors that do all the things you like and still have time to write grants and manage? If anything, I believe your work in a data-heavy field of science would allow you to "get your hands dirty" far more often than one in the life sciences.

Just some food for thought. Below are finally the specific answers you have asked for :)

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- Honestly, it sounds to me like being a tenure-track professor is your favorite career, but that you are scared of the risks. It's great to know the risks, but you must weigh them properly. I recommend asking tenure-track professors at your institution if they have such concerns, and find out how they work around them. I don't think it's quite as dire as you say!

- Also, with a data-science background you could have the best of both worlds: "embed" with faculty studying your disease area of interest, and be a part of their grants (or start a "bioinformatics core facility") to fund your salary. I have seen variations of this that provide with ample time to do your own research. And if it's a disease you are interested in, perhaps helping others with their data would make you happy?

[Dave Jensen]

Great advice from our Dave Walker . . . thanks Dave.

I'd second much of what Dave says, and add that there are plenty of places where you can incorporate your love of data along with your love of science. You don't have to move to the Insurance industry (ugh) or some weird alternative.

I've done a couple of searches recently for statistics people who combine science (disease plants, etc) with great data skills. They are very much in demand -- their software of choice appears to be "R" software.

Also, the field of ICT is interesting and requires data and IT expertise, and there are areas where the life sciences and ICT meet and become one.

I'm sorry no more specifics come to mind. I'll pop back if that's the case.

Dave

[PG]

Importantly I think that job safety today comes at least in part from being someone that it is possible to hire. Everything on your list of things that you like is attractive to the job market and if you can build at least some experience in that area you should be an attractive hire meaning that you can worry at least less about the risk of having to find a new job.

I have been in a start up company that laid off all staff. Thanks to my network and being someone that it was possible to hire this was shortly followed by a phone call that resulted in a new position.

[Dick Woodward]

Kaylee:

All of the advice that you have received is excellent. I would just like to second Dave Walker's comment about informational interviewing. This is by far one of the most powerful tools in your arsenal when looking at career directions. You would be well advised to read the suggested article and then give it a try.

Best of luck,

Dick

[Kaylee]

Wow -- thank you everybody for your awesome and thoughtful replies! You've definitely given me a lot to think over.


Dave, I wanted to clarify this part:

Also, I think you are forming biases that are not necessarily helpful. Do all epidemiology tenure-track professors in the world stay up at night worried with grants? Instead of focusing on the worst-case scenario, think about the job duties, and ask yourself if you like them. Have you written a grant yet, or helped a professor with one? It is a great way to see if you would like doing that.

I didn't mean to say it's like that for tenure track professors, since a lot of them are partially hard-money from teaching, have start-up funds, etc... what I was referring to was more the non-tenure-track 100% soft money "research professor" type positions (I've now updated my post to clarify that). On the one hand, those positions have the appeal of getting to spend all of your time on research, but they seem terrifyingly insecure...

As for tenure-track, I'm still interested in that option, but kind of alarmed to see how over-worked a lot of the faculty in my department are (I think 60-ish hour weeks are the norm) and how much of that time is spent on non-research activities. But maybe that varies by school/program...

- Also, with a data-science background you could have the best of both worlds: "embed" with faculty studying your disease area of interest, and be a part of their grants (or start a "bioinformatics core facility") to fund your salary. I have seen variations of this that provide with ample time to do your own research. And if it's a disease you are interested in, perhaps helping others with their data would make you happy?

Interesting... how does the "embed" option work? What sort of job titles/arrangements tend to go with something like that?

The "core facility" thing is being part of a team that's available to consult/help on others' projects as needed? But possibly still have time for some of your own research? I think I would LOVE doing that!

[D.X.]

Hi Kaylee,

Just a couple notes. First, epidemiologic research yields data that is associative or Shows linkage, not causality, however depending on your sample size and statistics applied you can get correlative data that is is still associative but powerful enough to Support cauality. Just a note, and example here is data coming from the Framingham Heart Study and Smoking linkage to cancer. Thats epidemiologic data, associative data, but so strong the correlation we can get to causality (of course followed by other data pointing the same direction). So consider that.

Alot of epidemiologic data is Patient Level data centered on outcomes and various associations that may underly those outcomes so some clinical Level knowledge is needed too. I think one of the great challenges with epdimiologic reserach is getting Access to a compreshensive set of data.

Enter the Need for identifying data-bases and Need to pool/merge those data sets, and thus probably where your coding Expertise can come in to Play. There is alot of Focus now on this, we call it real-world evidence and governments, industry, academia, and the ngos are all on board. The idea is to leverage those understands of disease etiology, Progression, and Intervention outcomes to best understanding efficient and effective preventions, Treatments, and of course optimized health economics. Personalized medicine with a genetic means to predict disease course and disease Treatment outcomes fits nicely here.

That being said with the above Background in Addition to the above recommendationn, if you are in the US, you can find some good careers on teh government side where what i described above is a hot Topic.

In the US look withing the Department of Health and Human Services (where the NIH sits) specific to the AHRQ or Agency for Healthcare Research and Quality. Go to their Website and see what you can learn - when I first engaged with them many years ago they were still finding their way I think now they have a direction - they work collaboratively with CMS.

Look at CMS as well - the good part there is you may have Access to alot of data from thier Medicare/Medicaid databases.

Another spot to look is the US Veteran Affairs. The US VA is impressive in that thier databases are quite comprehensive and the collect alot of data and most are interlink. This is one of my favorites in Terms of data and availablity - alot of epdimiologic studies are published using data from the US VA.

Another place is the US Public Health Service Commissioned Corps.- this is Military but I do believe they hire civlilans but also involved in epi research - you can go Military here and get some good Job benefits under that path.

I can't say much on the ex-US front other than look to the various National Agencies - at EU Level I would recommend you start at the EU Innoviative Medicines Initiative (IMI) and go from there. I am sure the various nations have similar Groups as in the US, i.e the UK has the NHS etc.

Of course all the above at first for your desk Level research and eventual move towards Networking and informational interviewing when you find a hotspot of interests.

Good luck!

DX

[Dave Walker]

More good advice all around; if you haven't considered government DX has a good insight. Job roles and job security are different within the government compared to the rest of academia, and I think most graduate students don't get enough exposure to it in their training. An excellent place for informational interviewing.

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Kaylee to your additional questions:

- "Research professor", non-tenure-track careers are to my knowledge a new phenomenon, and I am skeptical of them as a viable long-term career choice. I know they have been around since universities began in one form or another, but their increased popularity is new. I too worry that they are a "glass ceiling" on a research-only. However, not everyone has ladder-climbing career ambitions, and I have known some people in these roles who are happy to do research in a low-pressure environment while providing a stable income for their family. Others have applied and received grants that allow them to transition to the tenure track.

- Choosing the hours of a workweek is mostly a personal decision. In my humble opinion I do think academics tend to work long hours, but at a pace they set themselves. Freedom is the word for academia -- your life can be as busy as you want it to be. Putting a number of hours on it is difficult, because any career can have 60+ hours/week

- The "embedded" epi professors I have seen (a phrase I just coined, you heard it here first!) grew out of a relationship between a research group (say, in neuroscience) and a professor in the Epi department (and her postdocs, of course). It starts as a collaboration, which is also a top word in academia today; eventually you can be written into grants and can do your own research with datasets available to the researchers with you as PI.

- The "core facility" option is more of a formal arrangement, and probably involves working on others' data more than your own. Possibly including managing technicians to help with analysis. I have seen core facilities wherever there is a next gen sequencer on campus, either for a PI, department, building or campus-wide. Each arrangement is somewhat unique but I have seen it at many large institutions; you could probably find one at your institution or nearby and start your informational interviewing there.