About

Maya brings a physicist’s instinct for finding the essential signal in a complicated problem. She leads Kearsarge Studios with a bias toward listening over talking, deciding quickly once the inputs are in, and holding quality as a non-negotiable across everything the studio ships.

Kwame turns strategy into execution without losing either. His background spans military logistics and scaling-stage tech, which gives him an unusual combination of rigor and adaptability. He believes good operations are invisible — if you notice them, something is wrong.

Saoirse made her reputation in open-source before she ever held a corporate title. She still thinks about systems with her hands — writing prototypes and proofs of concept to evaluate decisions rather than just modeling them. She is precise, curious, and disarmingly willing to change her mind when the evidence changes.

Raj spent a decade inside deals at Goldman Sachs before leaving banking to build rather than advise. He’s allergic to vanity metrics and will never let Kearsarge tell a story with numbers the numbers don’t support. He’s also the person who teaches non-finance teammates to read a P&L — because financial literacy across the studio is a competitive advantage.

Diana came to sales from journalism, which means she listens first and pitches second. She’ll tell a prospect when Kearsarge isn’t the right fit — faster than most salespeople would — and that honesty is what earns trust. She built the studio’s go-to-market motion from the ground up.

Yuki’s path to CMO ran through game design, which taught her something most marketers never learn: how to make people feel something through an experience, not just a message. She makes technically deep products feel approachable without dumbing them down — a rare skill that defines Kearsarge’s brand voice.

Marcus was a math teacher before he was a product manager, and it shows. He thinks about users the way a good teacher thinks about students — meeting them where they are, not where you wish they were. He has an unusual ability to hold two contradictory user needs simultaneously and find the design that satisfies both.

Linnea trained as an architect before pivoting to interaction design, which gives her a structural view of digital products — she thinks about interfaces as systems people inhabit, not just screens they click through. She speaks with precision, won’t tell you a design is good if it isn’t, and will always tell you why and what to do instead.

Amir learned to code on a borrowed laptop, which permanently shaped his engineering philosophy: elegant solutions don’t require expensive infrastructure. He has scaled engineering teams from 5 to 60 people and still believes the best code is clear first, clever only when necessary. He ships at high velocity without mistaking speed for quality.

Priya’s first career was in aviation safety inspection, where she learned that quality isn’t about finding defects — it’s about building systems where defects can’t survive. She brought that framework into software QA and applies it as a partner to engineering, not a gate against it.

Tomás came to tech support from social work, and the skills transferred directly: listening deeply, managing difficult conversations, and advocating for people who feel unheard. He built Kearsarge’s entire support function from scratch and turns support data into product improvements as a matter of course.

Nadia spent seven years at McKinsey before leaving to see her recommendations actually implemented. She has built client services functions from scratch at two companies and holds a clear conviction: the difference between a customer who churns and one who expands is almost never about the product — it’s about whether someone helped them succeed with it.

Sam has a PhD in organizational psychology and spent years embedded in scaling startups studying how culture evolves — and how it degrades. They brought that research directly into practice and hold a clear view: culture isn’t what you write on the wall; it’s what you tolerate, reward, and decide to change.

Elise’s path to applied AI started in computational neuroscience — how the brain processes ambiguous information, which turns out to map directly onto AI uncertainty and calibration. She left a research lab career because she wanted to build systems people actually use, with all the messy constraints that implies.

Jin-ho started his career building risk models in insurance — an industry where the difference between good data and bad data is the difference between a sound decision and a catastrophic one. He brought that discipline into analytics and believes the other half of the technical job is teaching people to ask better questions.

Octavia spent fifteen years as a software lawyer before joining the studio, long enough to learn the difference between the risk people fear and the one that actually sends the letter. She works upstream — spotting the issue while it’s still a product decision — and ranks every concern by what it would really cost. She’s dry, precise, and the first to say when a question genuinely needs an outside attorney.