I taught high school math and physics for three years as a Teach for America (TFA) corps member between college and grad school. I have mixed opinions about TFA and its role in American education, but I recently did a small interview for them, and some of the questions were about how I use skills from my time in the classroom in my current career.

Jumping into first-year PhD classes after taking three years off was definitely a big challenge. Even though I had worked in Frances Arnold’s lab as a summer researcher while teaching, it turns out that first-year classes don’t have much to do with your PhD research, and having my undergraduate material on thermodynamics and fluid mechanics fresher would definitely have made things easier. As I got deeper into research and now into my first scientist job, transferable skills became clearer.

Consistently doing hard things well

While there were some long nights finishing problem sets or debugging code, on average I worked much less as a graduate student than I did as a teacher. For context, I often got to my school before 7am, taught 8am - 3pm, had meetings or coached robotics or caught up on grading until 6pm, then prepped for 2+ hours at home. There’s not much pressure like knowing you have 25 9th-graders waiting for you at 8am, and that any time you don’t have planned they’ll happily fill in with unproductive shenanigans, and then later they’ll be resentful if you do plan well and they don’t have time for unproductive shenanigans.

Being required to perform at a high level in the classroom every day pushed me to use my planning time well, to prioritize, and to develop sustainable procedures. While I don’t need to have such important outputs each and every day as a scientist planning and prioritizing are definitely essential when there’s so many interesting problems to solve, and different managers may need different deliverables on different timescales. Giving continual, steady effort is also an important skill during a PhD, especially if you work in a large group with a busy professor who won’t check in on you much but will expect results when she does.

Teaching, mentoring, and feedback

The first procedure I felt like I really mastered was designing assessments that I could grade quickly. This allowed me to spend more time on everything else while also providing students with the quickest possible concrete feedback on assignments. This carried over directly the TAing in grad school, as the primary responsibility in most of my classes was writing and grading assignments. As a TA, I also led discussions, held office hours, and gave an occasional lecture. Planning and executing these sessions directly used my experience planning and executing these sessions for high schoolers.

In addition to formal teaching responsibilities, graduate students also mentor undergraduate researchers and newer students looking to join their group. As an industry scientist, I still spend time mentoring and teaching newer scientists and receive weekly feedback from one or more managers. As a new teacher, my administration and more experienced teachers invested a lot of time in observing my lessons and then giving me useful, actionable feedback. This feedback was pretty much the only reason I became a competent instructor after my first year.

As a graduate student, I mentored one student who then used the experience to win an NSF Graduate Research Fellowship, and another who has since earned internships with Microsoft. While both these students were brilliant when I met them, I’d like to think I helped them along.

Communicating results

It doesn’t matter how good your work is if nobody understands it. In Frances’s group, students gave a formal group meeting presentation about their ongoing work twice a year. At my current company, I give a variety of updates to the machine learning team or the whole company. My classroom experience taught me to communicate complicated topics to an audience with a range of backgrounds. I know how to check for understanding to make sure I’m not boring people or leaving anybody behind. I definitely don’t get nervous about speaking in front of people anymore after doing it every day for 3 years.

Furthermore, making slides is an art. I got really good at making animations for physics and for stepping through math (because my handwriting is bad and it’s hard to watch your classroom while writing on the board). I still make fancy slides, but now it’s to explain machine learning to biologists and biologists to machine learning people.

Equity and inclusion

TFA’s mission is to end educational inequity. It turns out that, even outside the world of K-12 education, there are a lot of inequitable outcomes, and it’s very hard to unsee once you know to look. This is directly applicable when I’m helping to write recommendation letters for students, or involved in hiring at a machine-learning startup.