Along the poorly lit road from grad school to a nonacademic career, one is likely to encounter the advice, as I have, that a successful transition requires the clear articulation of how the skills developed in years of research will translate to the requirements of said career. And here you might protest, as I did initially, that these skills just do not translate:

Where exactly on my resume do I mention that I can perform survival brain surgery on a goldfish?

Does my intimate knowledge of the PCR enzyme selection in the Fisher catalogue qualify me to be anything other than a salesman of said enzymes?

I can perfectly time the lulls in my experiment to coincide with peak free food availability at the vendor fair – does that count for anything on the job market?

But a step back and a reflective eye reveal a host of skills ready and waiting for creative repurposing:

Years of journal clubs and judicious highlighter usage have taught us how to take in new information and quickly identify inconsistencies. When that information lies outside our interest (I’m looking at you, protein folding), we know how to target our attention to only the essentials. And after the third or fourth time that our PI responds to our lengthy data presentation with “So what?”, we become trained to distill our own findings into one salient message.

When we design a project, we start by assessing what’s already known. Only by laboriously stitching together the current body of knowledge can we find the holes, which then require their own assessment process: Will filling this hole advance the progress of the field? Has this hole remained a hole because it is technically impossible/ridiculously expensive/brutally boring to fill? More importantly, will anybody pay me to do this? As grad students we develop the ability to soundly evaluate the viability of a project, which I’d bet is a widely useful skill.

Where a CIA agent learns when to trust her own intuition, we learn when to trust the p value (a one-tailed test? But where is your a priori justification!).  We are no strangers to effect sizes and are not easily impressed by the latest study claiming a 20% reduction in flu rates (let’s see the statistical power and then we’ll talk). When a real world job needs data interpreted – and it very often does – we emerge from the phone both that is grad school, capes trailing and correlation coefficients in hand.

We learn soft skills, too. We work in teams, but are also held accountable for our individual projects. We manage other people (a shout out here to my undergrad for slicing all that tissue for me!), and others manage us. We develop a sense for how personalities can affect a project. Does your PI require a little ego massaging before you can ask for an expensive new piece of equipment?  Will the rotation student only put forth his best effort if he feels ownership of the project? Is your team motivated or intimidated by the threat of competition from another lab?

In my informational interviews, I try to draw out the skills that have translated to success in that person’s career. For example, I learned how self discipline and strong writing skills helped this freelance science writer, how a familiarity with technical reports was valuable in a technology transfer office, and how experience in data analysis enabled success in the field of organization development. This process so far has helped me to believe that while no one might care about how smoothly I can mount brain slices onto a glass slide (very smoothly!), I do have skills that will be valuable on the job market.