Contents · 4 sections+
There is a specific type of person that trading firms across Europe and APAC are quietly desperate for right now. Not loudly desperate — quietly. There are no LinkedIn posts about it. No job boards. No recruiters cold-calling with a JD attached.
Just firms, talking to other firms, asking if anyone knows anyone.
Here is what the profile looks like: someone who has actually run a market-making desk at institutional scale, who also has hands-on machine learning experience applied to live trading environments — pricing, execution, inventory optimization — and who has operated teams across both European and APAC time zones with genuine cultural fluency in both.
Three things. All three, at the same time.
The number of people on earth who fit that description is somewhere south of fifty.
I.This Is Not a Skills Gap
That framing matters, because the instinct in most organizations is to treat this as a training problem. Upskill the quants. Send the trading operators on an ML course. Build a bridge internally.
It does not work. Not because people are not smart enough, but because the career arc that produces this profile was narrow and largely accidental. Systematic traders who moved into ML applications did so in a specific window — roughly 2017 to 2022 — when the tools became accessible enough to experiment with but the field was still young enough that practitioners built genuine depth rather than consuming abstraction layers.
Most of the people who made that crossing are now embedded somewhere. They have been for years. They are not on the market. They are not on LinkedIn updating their status. They exist as known quantities in a very small world, and they move — when they move — because someone they respect called them directly.
The firms that understand this are not posting roles. They are identifying people and approaching them before a vacancy is formally declared.
II.Where the Talent Actually Is
London holds the largest concentration of relevant profiles — prop trading firms, systematic hedge funds, the infrastructure layer of the global markets ecosystem. The density is real, but so is the competition for it.
Singapore is smaller but growing, primarily inside the APAC arms of global banks and regional funds. Post-2023 expansion activity in the region has absorbed most of what was available in the open market.
Amsterdam — outside of a small number of dominant firms — produces excellent quant researchers but fewer market-making operators. The Dutch market is strong on the theoretical side and thinner on the execution depth.
Hong Kong had relevant profiles. The post-2020 talent migration thinned the pool considerably. Some of that talent landed in Singapore. Some left the region entirely.
The practical implication: the people you are looking for are geographically dispersed, largely invisible to standard search processes, and not responding to inbound approaches that do not come with specific credibility in their world.
III.What Firms Actually Miss
The mistake most organizations make is conflating availability with existence. Because the profile is hard to find through conventional channels, the conclusion becomes: the profile does not exist, or the bar needs to be lowered.
Neither is true.
The profiles exist. They are just not findable the way most talent acquisition functions are built to find people. Standard search processes optimize for visibility — people who are on the market, who have updated profiles, who respond to outreach. This profile is structurally invisible to that approach.
The second mistake is timing. The firms that are ahead of this cycle are not waiting for a vacancy to open. They are in ongoing conversation with the relevant talent pool before there is a formal mandate, before budget is approved, before the urgency creates pressure that narrows the options.
Urgency in this market is expensive. The people who know they are rare know how to price that knowledge.
IV.The Honest Version of What This Requires
Finding this profile requires direct sourcing with specific credibility in both systematic trading and applied ML contexts. It requires being known in the small world where these people actually talk to each other. And it requires a willingness to have conversations before there is a formal search — because by the time the search is formal, the best options may already be off the table.
The firms running the deepest technology agendas in trading right now are not competing on job postings. They are competing on relationships, on timing, and on the ability to have a credible conversation with someone who has no reason to move unless the opportunity is genuinely exceptional.
That is a different kind of search. Most firms are not set up for it.