- Hong Kong Hong Kong
DARMAX GLOBAL Hiring! Full Time Quantitative Research Analyst — Factor Modelling - Machine Learning in Hong Kong - Ricebowl
Quantitative Research Analyst — Factor Modelling - Machine Learning
DARMAX GLOBAL
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Job Description
Responsibilities
A leading quantitative investment firm is expanding its equity research effort in Hong Kong and hiring Quantitative Research Analysts across two complementary tracks — factor modelling and machine-learning signals . We want to hear from rigorous researchers at either end of that spectrum; tell us which track fits you.
Track A — Factor Modelling
- Construct and enhance cross-sectional equity factors across global universes
- Build and maintain factor and risk models, integrating point-in-time data and novel factor constructs
- Improve factor diversification and downside protection within multi-factor portfolios
- Produce performance attribution and analytics that support portfolio decisions
Track B — Machine-Learning Signals
- Develop cross-sectional equity signals using machine-learning methods across global regions
- Engineer signals from alternative data — and apply NLP / LLM-based text analysis to corporate disclosures and other sources
- Enhance and diversify multi-factor signal libraries, with a focus on low-correlation, downside-protective ideas
- Monitor signal performance and benchmark against historical distributions
You will have (either track)
- An advanced degree (MFE, MS, or PhD) in a quantitative discipline
- Around 2–4 years in quantitative equity research (exceptional recent graduates considered)
- Strong Python and statistical programming — and, for the ML track, machine-learning fluency (pandas, scikit-learn, XGBoost; R a plus)
- Hands-on experience with factor construction and/or factor and risk models, alternative data, and point-in-time datasets
- The research rigour to take an idea from hypothesis through robust out-of-sample validation to live signals
Why apply A research-led platform where strong ideas get backed and reach live portfolios, with the data and infrastructure to do the work properly. Highly competitive compensation.
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