Quantitative Research Analyst — Factor Modelling & Machine Learning

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

1. Construct and enhance cross-sectional equity factors across global universes
2. Build and maintain factor and risk models, integrating point-in-time data and novel factor constructs
3. Improve factor diversification and downside protection within multi-factor portfolios
4. Produce performance attribution and analytics that support portfolio decisions

Track B — Machine-Learning Signals

1. Develop cross-sectional equity signals using machine-learning methods across global regions
2. Engineer signals from alternative data — and apply NLP / LLM-based text analysis to corporate disclosures and other sources
3. Enhance and diversify multi-factor signal libraries, with a focus on low-correlation, downside-protective ideas
4. Monitor signal performance and benchmark against historical distributions

You will have (either track)

1. An advanced degree (MFE, MS, or PhD) in a quantitative discipline
2. Around 2–4 years in quantitative equity research (exceptional recent graduates considered)
3. Strong Python and statistical programming — and, for the ML track, machine-learning fluency (pandas, scikit-learn, XGBoost; R a plus)
4. Hands-on experience with factor construction and/or factor and risk models, alternative data, and point-in-time datasets
5. 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.