Earned, not inherited.

Richard spent five years building and leading the Asia-Pacific practice at a major Wall Street executive search firm — becoming the firm's top revenue producer in Hong Kong and its youngest Vice President. Based in Hong Kong for seven years, he placed senior portfolio managers, heads of trading, and quantitative research leaders across Hong Kong, Singapore, Tokyo, Dubai, Abu Dhabi, New York, and London.

He returned to New York to open Bayes Group's US office — bringing the same APAC network depth and institutional relationships to American clients and mandates. The firm now operates globally from both bases: sourcing talent worldwide while maintaining the region-specific knowledge that only comes from years on the ground in Asia. That network has also extended to the Gulf, where Bayes Group has placed senior quantitative leaders at sovereign wealth funds in Abu Dhabi and Dubai.

He founded Bayes Group to do the same work with fewer constraints: no quarterly targets, no geographic silos, no pressure to fill seats rather than find the right person. The result is a firm that competes — and wins — against much larger players by being more rigorous, more networked, and more honest about what a search actually requires.

Richard studied Political Science with a focus on game theory, alongside double minors in Brain & Cognitive Science and Journalism. He completed doctoral-level coursework in incentive design and infrastructure economics, and won the University of Rochester's Jesse L. Rosenberger Prize — awarded for excellence across upper-level interdisciplinary coursework — among other awards.

The interdisciplinary foundation informs an approach to executive search that is more analytical than most and more behavioural than any. The most quantitatively rigorous people in finance are not immune to anchoring, loss aversion, or the status quo bias — they simply maintain more sophisticated narratives for why those forces don't apply to them. Understanding the actual decision architecture is what separates a search that closes from one that stalls.

Thomas Bayes was an 18th-century mathematician and statistician who formalised a framework for updating beliefs in the face of new evidence. His theorem — a way of reasoning under uncertainty — became the foundation of modern probabilistic thinking.

The name reflects how we approach search. We start with a prior — everything we know about a firm, a role, a market, and a population of candidates — and update it continuously as new evidence arrives. We do not stop searching when we have found a plausible candidate. We stop when the evidence says we have found the right one.

This matters in a market where most searches are won by whoever has the largest database. Ours are won by whoever has the best model.

In quantitative finance, separating signal from noise is the only work that matters. The same discipline applies here. Most of what circulates in the talent market — recycled referrals, candidates in permanent availability, keyword matches against shallow databases — is noise. Our process is designed around a single objective: find the signal.