What are the responsibilities and job description for the Machine Learning Associate position at Bracebridge Capital?
Bracebridge Capital, LLC is a leading hedge fund manager with approximately $12 billion of net assets under management. The firm pursues investment strategies primarily within the global fixed income markets with the objectives of capital preservation and absolute return without significant correlation to equity, interest rate and foreign exchange markets. Established in 1994, Bracebridge manages private investment funds that serve endowments, foundations, pension funds and other institutional and high-net-worth investors.
Approximately 160 employees operate from our office located in Boston's historic Back Bay. The entrepreneurial and collaborative culture at Bracebridge rewards and supports motivated, dedicated, enthusiastic and intellectually curious individuals. We believe our firm's greatest asset is the people who work here.
We are currently seeking a Machine Learning Associate to join our growing data analytics and machine learning team. Our team focuses on creating algorithmic and machine learning solutions to open-ended problems with trading impact. The work is deeply collaborative with finance professionals and is an opportunity to explore applications of machine learning in total return portfolio management.
The primary responsibilities of this position will include working closely with Portfolio Managers to frame potential projects and understand trading needs. The ideal candidate will be dedicated to developing a deep understanding of quantitative and mathematical problems and proposing practical algorithmic solutions. Since the team works closely with trading floor personnel to assist with portfolio management decision-making, an interest in finance is essential, but prior experience is not necessary.
Responsibilities :
- Partner with portfolio managers to identify and define projects with concrete impact
- Lead and collaborate on research and data analytics projects within a team
- Architect and build pragmatic, scalable and rigorously tested solutions for supervised machine learning and time series problems
- Contribute to existing projects as well as train and deploy models for new initiatives
- Work closely with research and data team members to develop end-to-end systems compatible with existing infrastructure
- Research and keep up-to-date with machine learning trends and technologies and how they can be applied (LLMs, RAG pipelines, etc)
Qualifications :
