About this role
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.
- 构建干净的历史数据集，并维护时间点数据，以确保研究使用准确且完全可追溯的信息。
- 运行回溯测试和统计检查，并研究因素表现如何因市场制度而异。
- 实现每月信号产生的自动化，并提出流程改进建议。
- 在适当的情况下，开发 HMM、体制转换和强化学习模型。
- 构建仪表板以跟踪数据质量和战略绩效。
Requirement:
- Master’s degree or PhD in machine learning, computer science, statistics or a related quantitative discipline. Specialization in reinforcement learning is highly desirable.
- 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.
- 硕士学位或博士学位，机器学习、计算机科学、统计学或相关定量学科专业。
- 具有定量研究、数据科学或相关领域（行业或学术）的工作经验。
- 具有优秀的技术能力，在 Python 中拥有较强的编程技能并拥有机器学习库的经验。
- 熟悉定量建模、投资组合构建和股票市场。
- 拥有处理大型、复杂和替代数据集的经验。
- 优秀的英文口头和书面沟通技巧，能够向技术和非技术受众提出复杂的想法。
- 展现出在团队环境中有效工作的能力。
- 强大的解决问题的技能和智力好奇心；独立推动研究项目的能力。