Sergio P. Mendes spent his early years drawn to how systems work. That interest took him from a computer science degree at Sacred Heart University to nearly two decades leading finance, pricing, and revenue strategy across some of the largest commercial organizations in his sector. Today, he serves as Vice President of Commercial Finance and Revenue Management in New York, based out of Norwalk, Connecticut. He is Portuguese by heritage, a guitar player by habit, and someone who has consistently found that the most useful thing he can do in any room is help people understand what the numbers are actually saying. His path was not mapped in advance. It was built question by question, problem by problem, and team by team.
A Conversation with Sergio P. Mendes
What drew you to finance when your education started in computer science?
I didn’t plan it. I was always drawn to figuring out how things connected. In computer science, that meant understanding systems and logic. When I started working and found myself surrounded by business data, I realized it was the same impulse. Business has its own kind of logic. Finance is where that logic gets tested against reality. The more time I spent in that space, the more I wanted to understand it at a deeper level. The MBA came later, and it gave me the language to operate at the level where data meets strategy.
What keeps you motivated when the work gets difficult?
I try to stay focused on the problem itself rather than the difficulty of solving it. When I feel overwhelmed, it usually means I’m looking at something too large without breaking it into pieces. Simplifying the problem is usually the first step toward making progress. That approach has worked for me consistently. I also find that returning to the core question helps. What are we actually trying to decide? Once that is clear, a lot of the surrounding noise falls away.
How did you build confidence in rooms where the stakes were high?
Mostly through preparation. If I walked into a meeting with leadership and I understood the data thoroughly, I wasn’t guessing. I could follow the conversation wherever it went because I had built a foundation. Confidence, for me, was never about personality. It came from having done the work.
What role has curiosity played in your career?
It has been the consistent thread. I’ve always tried to understand how things work, not just whether they are working. That might mean asking why a number is moving the way it is, or why a particular market is behaving differently from the model. Those questions led me into pricing, then into revenue management, then into the broader commercial finance role I have now. Curiosity pushed me further into the work than I might have gone if I was just following a career plan.
What was the biggest risk you took professionally?
Taking on the finance function for the Continental Division at Pernod Ricard. It was a newly created structure. There were no established processes. I had to build the finance support for a 14-state division essentially from the ground up. That kind of ambiguity is uncomfortable. But it is also where you learn the most, because you can’t rely on existing systems. You have to think from first principles.
What has resilience looked like for you?
It has mostly looked like staying in the problem. There were projects that didn’t go as expected. There were models I was confident in that turned out to be missing important inputs. The response was always the same: understand what happened, adjust the approach, and keep going. I didn’t treat setbacks as reasons to change direction. I treated them as information.
What is something about leadership you had to learn the hard way?
That being right about something doesn’t automatically make others ready to act on it. I had to learn how to bring people along rather than simply present conclusions. The clarity of an argument matters less than the confidence of the people who need to execute on it. I became a better leader when I stopped optimizing only for analytical precision and started optimizing for organizational alignment.
What do you hope the work reflects, looking back on it eventually?
I hope it reflects that clarity is possible even with very complex information. That it’s not about having more data. It’s about understanding which data matters, and being able to explain it to the people who need to make decisions. If the teams I’ve worked with are better at that because of the time we spent together, I’ll consider it a career well spent.