The Accidental Start

My career in finance began almost entirely by accident. In 2018, fresh out of undergrad with an Economics degree, I took a job as a Performance Analyst at Parametric— one of the largest direct indexing platforms in the country — not because I knew what the role entailed, but because the company was growing fast, the colleagues I met impressed me, and I knew it was a place I'd be challenged and could grow. I was right on all counts.

The role involved calculating performance metrics and risk reports — largely by hand, with a lot of spreadsheets and a lot of room for error. My ECON 482 class had introduced me to R, and it didn't take long to see that I could automate most of what we were doing manually. So I got to work — and within weeks the team was seeing dramatic improvements in efficiency and a sharp reduction in errors.

Building something that genuinely changed how a team worked felt like magic. I was hooked immediately — and I knew I was barely scratching the surface of what technology could do.

Learning to Build

That work got noticed, and in 2020 it led to a move into a formal Quantitative Analystrole. I'd earned it — but I also knew how much I still had to learn.

I continued building systematic processes that supported both the Product and Strategy teams — analytical workflows that had to work reliably, at scale, for real clients. The step up in stakes forced a real step up in craft. I learned SQL, made the switch to Python, and started understanding what software engineering actually meant: design patterns, testing, version control, managing environments. I was writing smarter — but more importantly, I was learning how to think about building things.

After a couple of years, though, I started to feel a ceiling. I was growing, but not fast enough — and that scared me. Too early in my career to slow down, I began gravitating toward the hardest research projects and the sharpest minds working on them. I figured proximity to that caliber of work would accelerate everything. It did.

Where It All Came Together

In 2022, I joined a Systematic Multi-Asset Research Team at BNY as a Research Engineer — a $12B platformwhere quantitative models drove real investment decisions for portfolio managers every single day. I also traded Seattle rain for San Francisco sun, which didn't hurt.

As a Research Engineer, my mandate was to build the technology that made the investment process systematic — full-stack and end to end. That meant owning a broad surface area: from the data pipelines that fed our models, to the quantitative tooling the research team built on top of, to the PM-facing applications that turned research into daily decisions for portfolio managers and traders.

Working at the intersection of quantitative finance and software engineering meant that no two problems looked alike. One day designing a database schema to store and retrieve time-series data at scale; the next, building a production-ready Python library to standardize how the team constructed signals and ran analytics; the next after that, crafting a front-end application that gave portfolio managers a live window into their risk and performance.

What made this role transformative was the breadth. I had to become fluent across the entire stack — data engineering, backend services, and front-end development — while staying grounded in the domain logic of what the investment team actually needed. Everything clicked, and I became a full-stack engineer.

The Next Frontier

If the beginning of my story was about discovering that programming felt like magic, then AI feels like finding out the magic wand is real.

So I've done what I always do — jumped in headfirst. Experimenting with tools like Claude Code for agentic development workflows, running large language models locally with Ollama, and exploring where LLM tooling can accelerate the quantitative research cycle — not entirely sure where it all leads, but too energized not to find out. What once took weeks of careful engineering — data wrangling, model iteration, building interfaces — can now be prototyped in hours. For a field like systematic investing, where the bottleneck has always been the gap between research insight and working technology, that compression is enormous.

But what excites me most isn't just the speed. It's the potential. Developed thoughtfully and safely, AI can fundamentally change what's possible — not just in finance, but in how we build, how we learn, and ultimately the world we pass on. That's worth being excited about — and working hard to get right.