Quantitative Python Developer - Systematic Trading - J13085
Posted
Summary
A Python developer builds and scales backtesting engines, research infrastructure, and distributed data pipelines for a global quantitative investment manager in Hong Kong, working closely with quant researchers. Core stack is Python's scientific ecosystem (NumPy, Pandas, Polars), Linux, Git/CI-CD, with no prior finance experience needed.
Quantitative Python Developer - Systematic Trading - J13085
Our client is a premier global quantitative investment manager building technology- driven research platforms. In this role, you will design and scale high-performance research infrastructure, backtesting engines, and distributed data pipelines. Prior financial experience is not required.
Key Responsibilities
- Build and scale core backtesting engines, simulation tools, and portfolio construction frameworks.
- Design clean, high-performance Python APIs and libraries to integrate research workflows into production.
- Develop scalable processing pipelines and distributed computing solutions for massive financial datasets.
- Maintain software engineering best practices, including CI/CD, automated testing, and performance profiling.
- Partner directly with Quantitative Researchers and Data Engineers to translate research needs into production software.
Requirements & Qualifications
- Degree (BS, MS, or PhD) in Computer Science, Mathematics, or a related STEM discipline.
- Advanced proficiency in Python and its scientific stack (NumPy, Pandas, Polars) with a focus on performance optimization.
- Strong command of software design, data structures, algorithms, and Linux environments.
- Proficiency with Git, CI/CD pipelines, automated testing, and profiling tools.
- Open to tech, startup, or data engineering backgrounds—no prior finance experience required.
Preferred Qualifications
- Experience with distributed computing frameworks (Ray, Spark, Dask) or cloud platforms (AWS/GCP).
- Exposure to high-performance languages like C++ or Rust.
- Open-source contributions to scientific or numerical Python libraries.