Transition from academia to industry

For most of my twenties, I studied memory in a psychology lab: how we hold on to what matters and let the rest go. I designed experiments, ran the numbers, and tried to explain behavior from careful evidence. In 2025, I took that training somewhere new. I joined Success Academy, a network of public charter schools in New York City, as a customer experience researcher. The questions changed, from how memory works to how families and scholars experience their schools. The way I think about them did not.

Customer experience research asks a simple question for the people an organization serves: what is working, what is not, and what should change? At a school, those people are families and scholars. They tell us a lot, through surveys, phone calls, and the comments they write. My job is to turn what they say into something the school can actually use.

What I Work On

Usually I have a few projects going at once. One is a pipeline that pulls in the steady stream of calls and emails our family-support team gets, and uses AI to sort each one by topic, so the team can see at a glance what families are asking about. I also design surveys for families and for middle-school scholars, then dig into the results and connect what people say to the outcomes we care about. And I build models that flag which families are most likely to leave. More to the point, they show when to ask and what to ask, so we can catch that risk early.

A lot of the work ends up as something other people use every day. I build dashboards that let teams explore the feedback by school, topic, and time, and I set up reports that pull together the week's themes and land in the right inbox on schedule. Most of the job, really, is turning a messy pile of responses into a clear, honest picture.

The Toolkit

The work takes a wider range of tools than I expected. I write SQL to pull data from our warehouse and Python to clean it up and analyze it. I lean on AI language models to read and sort tens of thousands of open-ended comments (work that would take a person months by hand). I always check them against a sample I label myself, so I know how far to trust them. I also fit statistical and machine-learning models to find what predicts an outcome, and I build the dashboards and reports that put the answers in front of the people making the call.

What Carries Over

Honestly, the move has been less of a leap than it looked. A good experiment and a good customer experience study have the same backbone: a clear question, evidence gathered with care, and a conclusion that does not claim more than the data can hold. The lab taught me to decide what I am measuring before I measure it, to keep what people actually did separate from what I was hoping for, and to be honest about what I do not know. That matters just as much when the subject is a family choosing a school as it did when it was someone memorizing a list of words. The tools are new. The way of thinking is not.