Job Summary
As a Senior Associate in the quantitative research team, you will contribute to the research and development of alpha signals, portfolio construction methodologies, and risk models for global equity markets. The ideal candidate will have a PhD in machine learning, with a strong preference for expertise in reinforcement learning, and 0-3 years of relevant experience. You will collaborate closely with portfolio managers, technologists, and other researchers to translate research insights into actionable investment strategies.
**Job Responsibilities **
- Alpha Signal Development: Research and develop novel alpha signals using traditional and alternative data sources, enhancing the return forecasting models for stocks.
- Model Enhancement: Improve return forecasting models and portfolio construction frameworks for global equity markets with a focus on applying reinforcement learning and other advanced machine learning techniques.
- Data Analysis: Apply statistical, econometric, and machine learning methods to large, complex datasets to extract actionable insights.
- Research Integration: Work with technology teams to integrate research models into production systems and ensure robust implementation.
- Collaboration: Partner with portfolio managers and other stakeholders to translate quantitative research into investment decisions.
- Continuous Learning: Stay current with academic and industry developments in quantitative finance, machine learning, and data science.
**Required Skills, Qualifications and Capabilities **
- Education: PhD in machine learning, computer science, statistics or a related quantitative discipline. Specialization in reinforcement learning is highly desirable.
- Experience: Experience in quantitative research, data science, or a related field (industry or academic).
- Technical Skills: 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.
- Communication: Excellent verbal and written communication skills, with the ability to present complex ideas to both technical and non-technical audiences.
- Collaboration: Demonstrated ability to work effectively in a team environment.
- Initiative: Strong problem-solving skills and intellectual curiosity; ability to drive research projects independently.