About
How I ended up working at the intersection of data, systems, and real-world decisions.
My path into data
I started college as a Computer Science major because I liked problem-solving and building things. Over time, I realized I was less excited by theory for its own sake and more interested in how technical systems affect real people and decisions.
That led me to switch into Information Science. Once I made that change, everything clicked, my coursework felt more meaningful, my projects became more applied, and I started seeking out opportunities where I could work on real data with real constraints.
What shaped how I work
During my internship at Fannie Mae, I worked on a machine learning pipeline analyzing over a million loan records. At first, the work felt abstract — models, metrics, evaluation.
That changed when I saw outputs from the system being discussed in actual risk conversations. It became very clear that my job wasn’t just to build something that worked, but to build something people could trust and explain.
How I approach problems
- Who is using this?
- What decision does this support?
- What happens if we get it wrong?
I’d rather ship a system people understand and use confidently than one that looks impressive on paper but creates confusion downstream.
I’m most energized by work that sits close to real users, real constraints, and real consequences.
Explore more: