Location: Shanghai, China

Responsibility:

• Build clean historical datasets, and maintain point-in-time data to ensure that research uses information that was accurate and fully traceable.

• Run backtests and statistical checks, and study how factor performance varies across market regimes.

• Automate monthly signal production and suggest improvements to the process.

• Develop HMM, regime-switching and reinforcement learning models where appropriate.

• Build dashboards to keep track of data quality and strategy performance.

 

• HMM

 

Requirement:

• Masters degree or PhD in machine learning, computer science, statistics or a related quantitative discipline. Specialization in reinforcement learning is highly desirable.

• 5+ years working experience in quantitative research, data science, or a related field (industry or academic).

• Strong programming skills in Python; experience with machine learning libraries.

• Quantitative Modeling: Familiarity with quantitative modeling, portfolio construction, and equity markets.

• Data Handling: Experience working with large, complex, and alternative datasets.

• Excellent verbal and written communication skills in English, with the ability to present complex ideas to both technical and non-technical audiences.

• Collaboration: Demonstrated ability to work effectively in a team environment.

• Strong problem-solving skills and intellectual curiosity; ability to drive research projects independently.

   

• 5

• Python

We are hiring a Quantitative Researcher to join our team and work alongside fundamental investors in China equity investing. The role centers on keeping China equity data and history clean and reliable, researching and backtesting investment ideas, and automating the process so the team can consistently produce solid research.