A career in motion

I came into technology through the engineering door. Early in my career, I was drawn to systems — how they were architected, how they scaled, how they failed, and what made them resilient. That instinct toward systems thinking has never left me.

I studied at UC Berkeley, where I was surrounded by people who took ideas seriously and cared about craft. That environment shaped how I approach problems: with intellectual rigor, a willingness to go deep, and a bias toward understanding root causes rather than treating symptoms.

My early roles put me close to the code and close to the data. That proximity gave me something I've carried into every leadership role since: an intuition about what's actually hard, and a respect for the engineers and scientists who are doing the hardest work.

Two things happened early in my career that changed how I thought about building products. The first was working on a system at scale — I mean real scale, where edge cases aren't edge cases anymore, and where the difference between a good model and a great model isn't measured in percentages but in millions of experiences.

The second was realizing that the hardest problems in technology are organizational, not technical. Getting the right people aligned on the right problem, at the right time, with the right information — that's the actual work. The code is the easy part.

I also learned early that personalization is not a feature. It's a philosophy. It's the belief that every person who uses your product deserves an experience that reflects who they are and what they need — and that building that experience requires not just good technology, but good values.

Amazon was a graduate school in product leadership. The bar setting culture, the six-pager tradition, the obsession with customer-back thinking — these weren't just management frameworks. They were a way of approaching problems with discipline and humility.

Leading personalization on the Amazon homepage, and later the cart and experimentation systems, required a different kind of leadership than I had practiced before. I was managing at the intersection of ML science, engineering, design, and business unit priorities — all of which had legitimate claims on the same real estate.

I learned to build consensus not through authority but through clarity. If you can articulate a problem clearly enough, the path to the solution becomes visible to everyone. That skill — translating complexity into shared understanding — became the center of my leadership practice.

The experimentation work at Amazon was where I developed my deepest conviction about the relationship between rigor and velocity. Organizations that treat experimentation as a bureaucratic gate move slowly and make bad decisions. Organizations that treat it as a learning discipline move fast and make good ones.

At PayPal, I came face to face with the real complexity of consumer financial products — not just the UX complexity, but the decisioning complexity. Every payment involves dozens of real-time decisions: risk assessment, fraud detection, identity verification, funding source optimization, customer lifetime value estimation.

What struck me was how often these decisions were made in isolation, by separate systems with no shared awareness of the customer. The result was a payment experience that was technically correct but not intelligent — it didn't know who you were, how long you'd been a customer, what you'd just done on the platform, or why you might be hesitating.

Building Payment Ready was my attempt to change that. To create a unified decisioning layer that treated customer readiness as a state to be understood and anticipated — not a binary judgment made at the last possible moment.

This experience crystallized my belief that AI's most powerful role in consumer products is not in the interface. It's in the decisioning layer that shapes what the interface even shows you.

I'm at a point in my career where I'm thinking carefully about what I want to build next — and what kind of leader I want to be while building it.

I'm deeply interested in the next generation of AI-powered consumer products: systems that not only personalize the experience but improve the decision-making of the humans using them. Products that augment human judgment rather than replacing it. Platforms that create durable business value by creating genuine customer value — not by extracting it.

I believe the most important product work of the next decade will happen at the intersection of AI capability and human trust. Building systems that customers rely on, that businesses can sustain, and that hold up to ethical scrutiny — that's the work I want to do.

Seattle is home. I'm engaged in the local tech community, interested in what's being built here, and open to conversations about what comes next.