Hudson River Trading's head of AI said the era of whiteboard quants is over
We've spoken previously about how the most in-demand quants in finance today are also engineering hybrids; top firms want people who can work across quant research, development and infrastructure. Speaking on Bloomberg's Odd Lots podcast over the weekend, the head of AI at a major prop trading firm suggests that the market for purely mathematical quant stars is waning.
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"We're essentially asking people to be both good researchers and engineers," said Iain Dunning, head of AI research at Hudson River Trading. "In this age of AI, the distinction is pretty blurry." He said these kinds of people are very hard to find, but "we pay well for those people as a result." In the UK alone, Hudson paid over £900k ($1.2m) on average to employees working on trading, research and technology services in 2024 according to Companies House accounts.
In the past, Dunning said quants could "just whiteboard, then the coding [came] a little bit afterwards." Today, he said that "any kind of research idea you have is intimately connected to how you implement it."
Hudson River Trading isn't the only prop trading firm edging away from the whiteboard. Jane Street has been open about its love for machine learning in recent years and has adopted Python as a second language at the firm after OCAML.
In particular, quants who thrived on low-latency strategies have had to adapt; Dunning said "all the latency has been arbitraged for the most part... there's no more shortening of the wire." Hudson River operates in both medium and high-frequency strategies today, and said that the firm is focused on making "the smartest possible decision we can" at each different speed of response.
Although quants are expected to be better at coding, they're not replacing engineering and data teams. Dunning said that his team, which is focused on training Hudson River's models, would be "dead in the water without the teams at HRT who get the data to these systems and then the decisions to the markets." Instead, it's all about "optimizing the stack."
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