About this role

Location: New York, HK, China

Analyze diverse datasets across US equity and futures markets to identify quantifiable trading edges and discover actionable alpha signals in high‑ and mid‑frequency domains.
Conduct end‑to‑end research including alpha factor mining, feature engineering, model construction, backtesting, and strategy optimization with realistic transaction cost and execution modeling.
Design and run robust backtests, walk‑forward validation, and stress tests to ensure signal stability and economic significance.
Collaborate with execution, engineering, and risk teams to translate research into production‑ready strategies and support live deployment/monitoring.
Execute critical research initiatives that directly support trading decision‑making and performance attribution.

Bachelor's, Master's, or PhD in Statistics, Physics, Computer Science, Mathematics, or another quantitative field.
Proven hands‑on experience with high‑frequency US equity trading (experience with tick‑level data, microstructure, order book dynamics, and execution constraints is required).
Proficiency in at least one programming language: Python (preferred), C++, C#, MATLAB, or R.
Strong data engineering and numerical skills for handling large tick‑level datasets and building scalable backtesting pipelines.
Ability to read and synthesize academic/technical English literature and rapidly learn new technical domains.
Intellectual curiosity, rigorous analytical thinking, and structured problem‑solving skills; strong attention to model validation and risk controls.
Preferred
Competition awards (e.g., IMO/IPhO/ACM‑ICPC) or strong contest performance.
Publications in top‑tier peer‑reviewed journals or conferences.
Experience with low‑latency systems, co‑location, market data feeds, and execution optimization techniques.
Familiarity with cloud/container technologies and production deployment (Docker, CI/CD, etc.).

Quantitative Researcher / Portfolio Manager