Description

If you're a technically strong equities quant with genuine machine learning depth, this is a chance to join a newly built pod on a well-capitalised Asian hedge fund platform and help shape its research DNA from day one. This seat offers real impact, proximity to decision-making, and a clear path to owning more risk and P&L as you prove your edge.
The platform
Our client is an Asia-headquartered, multi-billion AUM hedge fund platform with a growing systematic franchise, focused primarily on equities. The firm has a strong institutional investor base, robust risk management, and a culture that blends research excellence with fast, pragmatic decision-making. You will be joining a newly formed systematic equities pod, backed by meaningful balance sheet, modern infrastructure, and senior leadership committed to building out quant capabilities in the region.
The seat
You will be one of the early quantitative hires in this new systematic equities pod, working directly with a senior PM and a small team of quants and technologists. The focus will be on alpha research and model development in equities, leveraging alternative data and machine learning to build and refine systematic strategies across Asian and global markets. Compensation is highly competitive, with meaningful upside for strong performers as the pod scales and P&L grows.
Key responsibilities

1. Design, develop, and backtest systematic equity signals and strategies leveraging state-of-the-art machine learning and statistical techniques.
2. Source, clean, and engineer features from traditional and alternative datasets, ensuring robustness and production readiness.
3. Partner closely with the PM and tech team to translate research into live trading models, monitor performance, and iterate quickly based on data.
4. Conduct rigorous empirical research on model behaviour, risk exposures, and regime sensitivity to refine the portfolio construction framework.

Must-have requirements

1. 1-5 years of hands-on experience in quantitative research or systematic equities (buy side, sell side, or top-tier proprietary trading environment).
2. MSc or PhD in a quantitative discipline (e.g. Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research).
3. Strong machine learning background, with practical experience applying ML techniques to real-world financial or high-dimensional datasets.
4. Solid equities exposure (e.g. stock selection, factor models, cross-sectional signals, or market microstructure in equity markets).
5. Proficiency in at least one major programming language used in quant research (e.g. Python, C++, or similar) and comfort working in a research codebase.
6. Fluency in Mandarin, with the ability to work effectively with stakeholders and data sets in Chinese.
7. Willingness to be based full-time in Hong Kong.

Nice-to-have / strong plus

1. Prior experience at a proprietary trading firm, or leading quant platform.
2. Experience working with Asian equity markets and related datasets.
3. Demonstrated contributions to live strategies, from research through to implementation and monitoring.

For passive candidates
You do not need to be actively on the market to explore this. If you are in a solid seat but feel under-leveraged, constrained on capital, or limited in your ability to push new research and models into production, this could be a meaningful step up in platform, autonomy, and economics. A short, confidential conversation might be enough to determine if it's worth progressing.

if you or anyone else you know is interested, please contact Rhythy Yeung - rhythy@darmaxglobal.com or you can also reach me via text at +852 5622 5422 for more information