The contents of this posts are inspired by a panel on exploring job options outside of academia at the Department of Psychology at Penn.Thanks to the four cognitive scientists turned data scientists and consultants Ting Qian, Jurgis Karuza, Christine Boylan, and Neil Bardhan, for their input.
In our last post, we interviewed two cognitive scientists who have decided to leave academia for jobs as science communication consultants and data scientists. Complementary to that post, we have assembled an advice shortlist in case you are contemplating to leave academia.
- Sit down and imagine life in and outside of academia as concretely as possible
You are not happy with your current situation, but you are not quite sure yet whether you actually want to leave academia. What would change? More security? More regular working hours? A more interactive job? First, sit down and make a list of all points you like and dislike about your academic job. Then, try to gather as much information as possible about jobs you’ve had in mind – read up and talk to as many people as possible (see also next point) to get as many ideas as possible and a concrete picture of what your day-to-day job would look like and what your mid-term and long-term perspective might be.
- Search for something that is connected to your research interests
Let’s face it, you have spent many years of your life on becoming an expert in your field. And there’s a reason for you to have done so, probably that you are or were passionate about it. Going into industry might be a radical change, but this does not mean that you cannot find a job as close to your expertise and passion as possible. You love data? Data science. You love writing? Science journalist. Your research topic is education and you love it? Startup developing new E-education techniques. With a bit of research and patience, it is quite likely that you will find an area where you can leverage what you have already accomplished and learned.
- Talk to everyone you know
You never know where opportunity is hiding – once you have decided to quit academia, it can be a good call to work your personal network and talk about it to virtually everyone you know that has a non-academic job. People have experience and connections and ideas, and they are often happy to share their knowledge and network. Networking, especially reaching out to people you haven’t talked to in a while, seems an awkward concept to many of us – but if you do it in a polite and friendly way, do not have a default expectation of being helped, and are ready to be as helpful as you would want others to be, there is not much you can lose.
- Adapt your CV and let non-academic contacts check it
CVs differ hugely by industry, but there are general rules of thumb that can be used to transition from an academic to a non-academic CV. Content-wise, you will want to think about what skills are important in the job you’re applying for. Obviously, your publication list should not be the centerpiece of your CV, but the results of selected publications could be useful for some industries. In general, you might want to put an emphasis on practical skills with concrete, easy to understand examples as well as use clearer and more simple language than in an academic CV.
- Have a strong social media presence
Having an up-to-date LinkedIn is a no-brainer for most industry jobs. So have one, list your relevant skills and work experiences, and connect people that could be interesting for you. An active Twitter account where you tweet relevant pieces of information to your industry and follow key players, or a blog with informative entries on your industry can also help making contacts and getting known. Finally, if you are venturing into data jobs, having a github account where you showcase your skills can be a great reference. All of these are easy to set up and do not take too much time to curate at a basic level.
- If you want to venture into data science…
This is a special point for aspiring data scientists, since this is quite an obvious choice for the data-loving subset of cognitive scientists – requiring the skills to understand data sets quickly, know what information can be extracted from it, and actually perform analyses. In addition, since a large amount of the data that awaits analyzing out there is data connected to human behavior in some way, our background will also help us to deal with such datasets.
So if you play with the idea of becoming a data scientist, here are some pieces of advice:
- Check out bootcamps like Insight.
- R/Python/SQL skills are a plus and/or indispensable
- Publish stuff on github
- Prior to an interview, practice whiteboarding (coding freestyle on a whiteboard) and coding spontaneously in general – that is what you’ll often be asked to do.
- Take in this truth
Many people have left academia. And they are alive.
(Thanks to Ting Qian for this wonderful statement).