Machine Learning Analyst

Bracebridge Capital, LLC is a leading alternative asset 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 170 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 seeking a Machine Learning Analyst to join the growing data analytics and machine learning team. The team’s primary mission is to develop machine learning systems to answer open-ended investment questions and support portfolio management decisions. The team works across the project lifecycle and technical stack, from ideation, understanding and analyzing data, hypothesis generation and testing, and model development all the way through to the deployment and maintenance of models and systems in production.

Our work spans statistical modeling, classical machine learning, and modern AI and LLM techniques.

The Machine Learning Analyst’s primary responsibility will be to contribute to these efforts alongside other team members. Over time, you will develop the technical and domain expertise needed to take increasing ownership of individual components and ultimately end-to-end projects.

The work is highly collaborative and spans quantitative research, software engineering, and machine learning. Analysts work with other members of the machine learning team and portfolio managers to translate loosely defined investment ideas into practical tools and models. Successful candidates will have solid programming foundations, be comfortable translating between qualitative and quantitative descriptions of problems and be excited to build data analysis and machine learning systems against the backdrop of portfolio management.

Since the team works closely with trading floor personnel to assist with portfolio management decision‑making, an interest in economic and financial markets is essential, but no specific prior experience is necessary.

This role is open to candidates available to begin in the near term, as well as students expecting to complete their undergraduate degree between Fall 2026 and Summer 2027. Start dates will be determined based on candidate availability and, for students, degree completion.

• Collaborate closely with Machine Learning team members, portfolio managers, and researchers to translate open-ended investment questions into well‑defined analytical and machine learning problems
• Develop and evaluate data‑driven machine learning and quantitative models, including simulation‑ and optimization‑based approaches, for investment‑related problems
• Contribute to maintaining existing models and analytic tools in production
• Over time, take ownership of individual features and components and full projects
• Clearly document and communicate methods, assumptions, results, and limitations of models to other researchers and trading professionals across the firm
• Stay current with relevant new techniques and technologies in machine learning and artificial intelligence, particularly as they pertain to finance and investing

• 0-2 years of experience through industry internships, undergraduate research or thesis, or substantial independent technical projects involving software development, data analysis, or machine learning
• Proficiency in Python and familiarity with the Python data science stack (Num Py, Sci Py, Pandas, scikit‑learn, etc), with experience in other languages a plus
• Experience working with and analyzing data from multiple sources and in multiple formats
• Familiarity with machine learning and statistical modeling fundamentals, including model…