Citadel broadens quant hiring from AI research labs – report  

Citadel plans to expand its quantitative investment team and recruit researchers from AI laboratories as hedge funds seek talent also targeted by technology companies, reported Bloomberg.  

The group has about 180 financial researchers and data engineers.  

Its headcount is expected to grow at a “healthy” double-digit rate over the next year, Navneet Arora, head of global quantitative strategies, said. 

Citadel is continuing to recruit university graduates while also approaching researchers at AI laboratories including Google DeepMind. It is seeking people who can develop investment ideas and apply AI systems to trading. 

Arora said the increasing use of AI has not removed the need for human researchers. In his view, the technology is reducing the importance of coding expertise while increasing the value of people who can create investment ideas and direct automated systems. 

“People can now effectively manage two, four, and in some cases even more processes. Humans are now also becoming ‘managers of machines’,” said Arora.  

“Those humans, unlike machines, are not easily replaced.” 

Citadel’s Tactical Trading fund, which combines fundamental equity investment with quantitative strategies, was up 24.7% this year through August, according to a person familiar with the matter.  

Citadel manages about $76bn across equities, fixed income, and commodities. It currently assigns “multi-billion dollars” to quantitative strategies.  

Arora said additional hiring would support the firm’s equities business and the development of strategies covering futures and volatility. 

Arora said machine learning has been adopted more extensively in some areas of trading than others. Short-horizon strategies can draw on large datasets covering trading volumes and price movements. Their advantages can be narrow and easier for competitors to replicate, allowing excess returns to fade quickly. 

Longer-horizon strategies use less readily available data and more complex models to produce signals, according to Arora. He said this can make their advantages more difficult for competitors and machines to identify and could allow them to last longer. 

Arora said the firm’s approach links recruitment with investment in trading infrastructure. He said the infrastructure can reduce market footprints and help protect proprietary strategies, but that the higher cost of technology and talent makes scale more important. 

“The fixed costs of running the business have risen. Technology, talent, everything is more expensive,” said Arora. “Scale therefore matters more than ever, and I’ve encouraged my team to continue to think bigger and go bigger.”