Complex models become workflows people trust.

My career began with a fascination for understanding how businesses, markets, and institutions work.

After completing a B.A. in Business Economics at Universidad Torcuato Di Tella, I earned an M.S. in Finance there as well. My thesis explored real options valuation—a framework for making decisions under uncertainty by recognizing the value of flexibility. Long before I entered product management, this reinforced a principle that still shapes my thinking today: every meaningful decision involves tradeoffs.

I then worked in economic consulting, analyzing policy and building financial models for governments and private-sector clients. I quickly began writing software to automate repetitive analyses and reports, discovering that technology could amplify understanding rather than simply save time.

Those early years taught me to think beyond individual companies. Markets became systems of incentives, expectations, and human behavior. I realized that understanding those systems was often more valuable than simply memorizing financial metrics.

I supported Bank of America Merrill Lynch investment-banking teams through analytical and financial-modeling work—not as a Bank of America employee, but embedded with the team. That included extended periods in New York working alongside them. Through that collaboration, I learned high-yield credit analysis from one of the industry’s leading institutional investment groups before transitioning into equity research. I developed the discipline of evaluating businesses through both quantitative analysis and qualitative judgment.

Later, I joined TPCG, where I was given responsibility for my own coverage of Argentina’s regulated utilities and corporate debt markets.

The work extended far beyond valuation.

I helped international investors understand one of the world’s most complex investment environments—combining inflation, currency controls, regulation, sovereign risk, and political change into investment frameworks that clients could actually use.

During this time, I covered some of Argentina’s earliest renewable energy issuances, including Genneia, as the country began attracting new investment under a changing regulatory environment. I also followed companies such as Transener, where combining regulatory analysis with business fundamentals led to recommendations that generated returns exceeding 200%.

The experience taught me that people rarely struggle because they lack information.

They struggle because they need a way to organize complexity into decisions they can confidently make.

I came to the United States to attend Duke University’s Fuqua School of Business because I wanted to move from analyzing businesses to building them.

After graduating, I joined Zest AI, then a rapidly growing startup helping financial institutions adopt machine learning for credit underwriting.

This was years before generative AI made conversations about explainability and black-box models mainstream.

Our challenge wasn’t simply building accurate models.

We were asking lenders to rethink workflows that had remained largely unchanged for more than fifty years.

Success depended on helping organizations trust AI—not simply deploy it.

I began on the customer-facing side of the business, helping lenders operationalize machine learning inside highly regulated environments. Working closely with customers gave me a front-row seat to the organizational, technical, and human challenges of AI adoption.

As the company matured, I transitioned into Product Management.

Over the following years I helped launch and expand products across the lending lifecycle, including internal AI tools, Lending Intelligence, policy simulation capabilities, workflow improvements, and governance features that balanced automation with human oversight.

Many of the ideas that now define how I think about product emerged during this period.

The most successful AI products weren’t necessarily those with the smartest models.

They were the ones that improved judgment, created trust, and fit naturally into how organizations actually made decisions.

After Zest AI’s exit event and my departure from the company, I decided not to immediately jump into another role.

Instead, I took time to reflect on the work I had found most meaningful.

Across consulting, investing, and AI, I realized I had been solving the same problem from different angles:

How do we help people make better decisions in complex systems?

That realization became the foundation for the projects I’m building today.

Compass explores structured reflection, helping people think more deliberately about their goals, decisions, and personal growth.

Record Library encourages a more intentional relationship with music by introducing thoughtful friction into an experience typically optimized for speed and endless consumption.

At the same time, I’ve been writing about product judgment, AI adoption, decision design, organizational trust, and the role technology should play in improving—not replacing—human thinking.

These projects are different from the enterprise software I’ve built professionally.

But they all pursue the same idea.

Technology creates the greatest value when it helps people better understand complexity, exercise better judgment, and act with greater confidence.