Position Overview
You will serve as the critical bridge between our quantitative research and technology teams, responsible for transforming quantitative strategy prototypes into highly stable, high-performance production-grade code that directly supports live trading operations.
Key Responsibilities
- Refactor, optimize, and productionize researchers’ strategy code; build automated data processing pipelines and strategy execution workflows
- Continuously improve system performance, including memory management, computational efficiency, and parallel processing of large-scale datasets
- Maintain data infrastructure (databases, data pipelines) to ensure data accuracy, completeness, and low-latency accessibility
- Implement software engineering best practices including unit testing, code reviews, and continuous integration/deployment (CI/CD) to guarantee code quality
- Work closely with researchers to understand statistical models, factors, or optimization algorithms, and accurately translate them into production-level code
Required Qualifications
- Strong proficiency in Python or C++, with hands-on experience in data analysis libraries such as NumPy, SciPy, and pandas
- Solid software engineering practices (version control, testing, CI/CD, refactoring)
- Familiarity with Linux environments and Shell scripting
- Working knowledge of SQL and common data pipeline tools (e.g., ArcticDB or similar time-series databases)
- Foundational mathematical knowledge in statistics, optimization theory, or factor modeling, with the ability to understand research logic
- Strong interest in financial markets and quantitative strategy development
- Fluent in both Mandarin and English
Preferred Qualifications (Plus)
- Prior experience in quantitative development, systematic trading, or financial front-office technology
Morgan McKinley