Quantitative Researcher / PM - Systematic

Macro:

Key competencies

• Advanced degree in Computer Science, Statistics, Mathematics, Finance/Economics or related quantitative field from a top-tier institution
• Passion in investment research with strong intuition and the ability to think-out-of-the-box; strong record of original research and demonstrated problem solving ability
• Excellent quantitative skills from training in econometrics or statistics, and extensive experience in utilizing those skills in empirical research. Practical exposures to LLMs and AI agents a plus
• Strong programming skills analyzing large and complex data with statistical tools (Python)
• Proficiency in risk parity portfolio construction, volatility modeling, derivatives and leverage instruments; knowledge of macro and cross-asset dynamics in China
• Confident communicator, able to explain complex ideas in simple terms to both external and internal stakeholders
• 2-5 years of experience in researching and live trading alpha signals for futures in Chinese market

What you will be doing

Develop and improve risk-parity and macro quantitative investment strategies across financial markets within the dedicated China research team. Take ownership of the full investment lifecycle — from alpha generation and portfolio construction through to trade execution and implementation — with a particular focus on risk-balanced allocation frameworks across asset classes. Work closely with team members to create and support strategies by:

• Alpha Generation & Strategy Development: Identify new investment ideas and innovative data sources across macro themes, asset classes, and risk factors relevant to risk-parity frameworks.
• Data & Modeling: Gather and refine complex datasets; apply statistical analysis and quantitative modeling to build and improve risk models, correlation frameworks, volatility estimation, and return forecasting models central to risk-parity construction.
• Portfolio Construction & Implementation: Participate in research on risk-budgeting methodologies, dynamic leverage, diversification, and rebalancing strategies, as well as trading cost models and execution optimization.
• Interpretation & Decision-Making: Synthesize and interpret model outputs to drive portfolio decisions, with the ability to clearly communicate the rationale, risk exposures, and performance attribution of strategies to internal stakeholders.
• External Representation: Serve as a credible and articulate spokesperson for the strategies externally — engaging with investors and distributors to present investment philosophy, portfolio construction logic, performance, and risk management in a compelling and transparent manner.

ML/AI:

Key competencies

• Advanced degree in Computer Science, Statistics, Mathematics, Finance/Economics or related quantitative field from a top-tier institution
• Passion in investment research with strong intuition and the ability to think-out-of-the-box
• Background in core ML theory: optimization, probabilistic modeling, deep learning architectures (transformers, diffusion models, etc.)
• Strong publication record or thesis work in ML/AI (conference papers at NeurIPS, ICML, ICLR, ACL etc. are preferred); hands-on experience in fine-tuning large-scale foundation models
• Experience with cloud environments and multi-machine CPU/GPU setups
• Ability to communicate complex ideas clearly, strong collaboration skills to partner with other teams to implement ideas into products
• Prior experience in the investment field is a plus but not required

What you will be doing

Develop and improve AI-driven quantitative investment strategies in financial markets within the dedicated China research team. Support all strategies from alpha generation through to trade execution and implementation, working closely with other team members to design, build, and enhance strategies using cutting-edge machine learning and AI techniques. Core responsibilities include:

• Alpha Generation & Idea Discovery: Leverage large language models (LLMs), NLP, and alternative data pipelines to identify novel investment signals and innovative data sources
• Data Gathering & Refinement: Apply AI-powered data processing and feature engineering techniques to gather, clean, and structure complex datasets for modeling at scale.
• Modeling & Statistical Analysis: Design and implement deep learning, reinforcement learning, and statistical ML models to build, backtest, and refine various quantitative models.
• Interpretation & Presentation: Translate complex AI model outputs into actionable investment insights, ensuring model interpretability and explainability when communicating results to stakeholders.
• Implementation Research: Conduct research on AI-enhanced implementation aspects of investment strategies, including ML-based trading cost models, learned risk models, AI-assisted portfolio optimization, and dynamic portfolio construction.