Five-plus years in mortgage lending and fintech operations, now pointed toward machine learning and AI-driven operations. Two ways of working keep showing up, so I named them.
Find what is broken, measure it, fix it, check the numbers again.
I treat operations like an engineering problem. At Nuestro Financial, I built a customer operations function from nothing and drove down error rates and delinquency through measurement and process discipline rather than guesswork. I write the standard, hold people to it, then go back and check whether it actually worked.
I also like catching problems before anyone else sees them. Whether it was a system quietly miscounting what customers owed or a portfolio figure that didn't reconcile with reality, I've made a habit of tracing the discrepancy back to its source and handing leadership numbers they could actually defend, not just numbers that looked right.
That same discipline, treating a messy process like a system that can be measured, debugged, and improved, is exactly the mindset I'm now applying to machine learning and AI-driven operations. Different tools, same instinct: find what's broken, measure it, fix it, check the numbers again.
Customers, vendors, regulators, and machines rarely speak the same language.
English and Spanish are both native to me, and I'm conversational in Mandarin and still studying it. That instinct for translation has never stayed confined to conversation. Throughout my time in fintech operations, I've repeatedly found myself the person closing the gap between what customers need, what a system does, and what a regulator requires, often before anyone had to ask.
I've led cross-functional teams through market entries and platform launches, coordinated outsourced vendor teams across borders, and managed relationships that only worked because someone was willing to meet people where they were, sometimes literally, on the ground in another country. Closer to home, I serve on the board of the NAHREP Nashville chapter, handling its finances and governance.
That same instinct, reading the pattern underneath the noise and building the structure that closes the gap, is what's drawing me toward machine learning and AI-driven operations now. The languages have changed from Spanish and Mandarin to data and models, but the work is the same: translating what people need into something a system can actually deliver.
I speak English, Spanish, and Mandarin (still working through textbooks to keep the last one sharp). I studied finance at the University of Tennessee at Chattanooga, then spent the last several years in mortgage lending and fintech operations, building processes and leading teams through complex regulatory and technical environments. That work put me close to the data, automation, and vendor systems behind the scenes, and it's what's driving my current shift toward machine learning and AI-driven operations, where I'm applying the same process-first mindset to a new set of tools.
不积跬步，无以至千里. Without accumulating small steps, you cannot reach a thousand miles. (Xunzi)