Quantitative Researcher

Anson McCade; Hong Kong; On-site; HKD 900,000 - 1,400,000; Full time

Job summary

Anson McCade is seeking a Quantitative Researcher to tackle mathematical and statistical problems at the intersection of statistics, machine learning and large-scale computing.

This greenfield environment offers researchers freedom to shape the research agenda and the platform around it, with no legacy systems holding back exploration. Exceptional problem-solving, strong academic or competitive records, and ability to move between theory, experimentation and implementation are essential.

Qualifications

• Exceptional mathematical and statistical problem-solving ability.
• Intellectual curiosity and the ability to conduct independent research.
• Ability to move between theory, experimentation and implementation.
• Exceptional academic or competitive track record.

Skills

Mathematics

Statistics

Machine Learning

Research Skills

Competitive Programming

Education

PhD completed or near completion

Tools

GPU Computing

Job description

Quantitative Researcher - Maths / Statistics / Machine Learning

We are working with a newly established research and technology firm building a next-generation predictive trading platform from the ground up. The firm is being built by a small team with backgrounds across Citadel Securities, Jump Trading and other leading quantitative and technology environments. They are now assembling a highly selective research team to tackle complex prediction problems using a combination of classical statistics and modern machine learning.

Previous experience in finance or trading is not required. In fact, we are particularly interested in speaking with researchers and exceptional graduates currently working outside of financial markets.

The opportunity

• The research sits at the intersection of mathematics, statistics, machine learning and large-scale computing.
• The team is exploring predictive problems using approaches spanning statistical modelling and inference through to deep sequence models, reinforcement learning and GPU-scale training.
• This is a genuinely greenfield environment. There are no legacy research systems or inherited processes - early researchers will have significant freedom to explore ideas and influence both the research agenda and the platform being built around it.
• The existing team combines significant experience across quantitative research and technology with exceptional academic backgrounds, including International Olympiad medals and top Putnam performances.

Who we're looking for

We're interested in exceptional problem-solvers rather than a particular industry background. Relevant backgrounds could include:

• Mathematics
• Computer Science
• Physics
• Operations Research / Optimisation
• Computational Science
• Applied Mathematics
• Other highly quantitative disciplines
• Completing or recently finished a strong PhD
• A researcher with a few years' experience in AI, deep-tech, scientific computing, technology, biotech or another research-led environment
• An exceptional undergraduate or Masters graduate with outstanding academic results
• An Olympiad / competitive programming candidate with IMO, IOI, IPhO, ICPC, Putnam or comparable achievements

We're looking for evidence of:

• Exceptional mathematical and statistical problem-solving ability
• Intellectual curiosity and an ability to conduct independent research
• The ability to move between theory, experimentation and implementation
• An exceptional academic or competitive track record

You do not need to know how financial markets work. The team can teach the domain; the priority is finding people with exceptional raw research ability.

Why consider it?

• You’ll be joining at the very beginning of the build rather than entering an established research organisation.
• The firm is deliberately building a small, talent-dense team where researchers can take ideas from inception through to implementation without layers of infrastructure or organisational complexity.
• Researchers will have access to petabyte-scale datasets, GPU-accelerated training and large-scale simulation, with significant resources available to test ambitious ideas.
• Although trading is the initial application, the longer-term ambition extends beyond trading alone, using the technology, research and team being built to tackle a broader set of problems across AI, finance and technology.