Founder Note
Katherine Osella
Fashion has always reached me from two directions. My family is half British — opinions on what's directional, what's derivative, and what's simply not done arrived alongside everything else. As a dual US–UK citizen now based in London, I've watched the same trends land differently on opposite sides of the Atlantic — what reads as directional in New York arrives already absorbed in Paris, what feels niche in London turns out to be mainstream in six months everywhere else. That gap between markets is where the interesting questions live.
I studied philosophy at Harvard, which turned out to be the right training for this kind of work even if it wasn't obvious at the time. Philosophy taught me to look for the assumption underneath the assumption — to ask not just what a trend is doing but why it has authority, not just what a ranking shows but what it's structurally incapable of seeing. That habit of finding the question underneath the question followed me into analytics, where I spent two years at Northside Hospital modeling market demand and physician networks, and consulting work at Four Points Health before that. I learned to build signal models, run geospatial analyses, and turn genuinely ambiguous data into decisions that senior executives would stake real money on.
Fashion is the domain where these instincts found their sharpest application. Not just as a consumer category — as an information system. The way a silhouette moves from a show in Paris to a buying office decision to a shop floor in six months is one of the most compressed and human prediction problems there is. It's about taste, about timing, about understanding why a certain customer will want something before she knows she wants it. That's not a data problem. It's a judgment problem that data can sharpen — and philosophy and analytics together turned out to be reasonable preparation for it.
Maison Première is my attempt to think seriously about both sides. The methodology is rigorous — empirical Bayes scoring, six independent signal components, explicit modeling of commercial opacity across houses with very different disclosure strategies. But the questions underneath it are the ones I actually care about: what does it mean for a signal to have genuine cultural authority versus manufactured heat? When does a trend convert and when does it stall? What can runway tell us about desire that search data can't, and vice versa?
I'm interested in roles at the intersection of fashion intelligence and commercial decision-making — analytics, buying, trend, and strategy functions where taste and data work together rather than past each other. Based in London. Open to the full spectrum of how fashion businesses think about what comes next.