Launching into a brand new career in business after a decade focused on academic learning continues to throw me off. Did I really need to get a PhD in microbiology and immunology to take a part time job in marketing? Couldn’t I have just done four years in undergrad and be six years into this career, instead of just now starting out in an entry level position?

When we leave a grad program, we will often get an “exit survey” to ask where we plan to go next. Sometimes this is for internal tracking, sometimes it’s for the benefit of the university boasting about their students’ bright futures. Regardless, one question that stood out was whether I would be continuing in science and if I would be using the skills I learned in my PhD. I went with “yes” because I didn’t want to think about it too much, and working at this company allows me to interact with scientists and science-entrepreneurs. . . so, close enough?

Luckily, it’s turning out that I actually am using some of my basic training in problem solving for my day-to-day responsibilities (rather than just during weekly meetings to discuss new potential clients’ projects).

Most Of My Job Is Still To Create Hypotheses, Test Them, And Report Back On My Findings.

. . .

In my thesis research, I read tons of papers about how microbes work together to build structures and how they protect themselves from invaders. I took notes and created a few different guesses on how this existing knowledge could be applied to how bacteria interact with plant roots in mixed groups. With these guesses based on prior information (hypotheses), I designed ways to directly test my assumptions. How could I figure out which guesses were wrong? What experiments would allow me to cross out options until only a few remained? Which were the best tracks to start on, based on likelihood, time to completion, and interpretations of possible outcomes?

In my research, it was essential to start out with how I’d measure the outcomes. If I waited until the end of the experiment to interpret the data, I would be likely to pick out certain trends that were most interesting. Or I would find that I’d forgotten to include important variables to allow me to make conclusions about what I saw.

Once the experiments were completed, I had to collect the data and put it into words and diagrams that could be explained to others. These reports needed to include why I’d worked on this project, how I’d conducted the tests, what information came out, and how this helped to narrow down possible answers. The research wasn’t done until it was clearly communicated.

Now, as I work to figure out how we can best reach out to clients and support those who we’ve already worked with, the process is strikingly similar.

Rather than reading scientific papers, I read blog posts and watch YouTube videos. Instead of reaching out to professors or students, I contact experts in marketing and PR. Instead of measuring how many bacteria stick to a plant root under different conditions, I track how many people open a certain type of email and what percentage get to the website. Whether the output is clumps of bacteria in a petri dish or the number of web-to-lead conversions, I start out with a clear plan of what I will measure. And then I present the work to my boss in a way that he can understand and appreciate, just as we did in lab meetings.

. . .

In both situations, the process of guess-check-rethink (Aka, the scientific method) continues; grad school also taught me to accept and embrace this. The best experiment is one that both 1) answers your original question in some way, and 2) prompts you to rethink your hypothesis and design new testable experiments. I didn’t realize how difficult this used to be for me back in 2014 (“you mean I have to do this forever and it’s never good enough?”) to now (“ cool, now I have new options!”) It’s only been through trial and error, immense frustration, and guidance from folks much smarter and more experienced than I to get to this space.

Thinking back, grad school has changed how I think about a lot of things. Instead of trying things and then checking what happened, I try to start out with a guess based on what I already know. I decide how I will measure the outcome. And then I deliberately assess what occurs based on these metrics.

I’ve Taken For Granted That Everyone Faces Problems With This Same Perspective Of “What Do We Know, What Do We Don’t, What Can We Guess, And How Do We Test It?”

Rather than saying grad school in research is meant to teach us to “think like a scientist” maybe we should say that it trains us to think in (literally) logical directions. Knowing how to actually use logical thinking is a skill that I have to appreciate as just one example of how I really am still using my PhD training.