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 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 Analyst 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 other members of the Machine Learning team to execute on projects across the full lifecycle, from concept to production. The ideal candidate will be eager to build a strong understanding of quantitative and mathematical problems and to learn how practical, data-driven and algorithmic approaches can be applied to real-world investment challenges. Strong candidates will be excited to grow over time, by developing deep domain knowledge in financial markets, increasing ownership of models and systems, and taking on broader responsibility as their skills mature.

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:

• 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 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.

Qualifications:

• Bachelor’s degree (or equivalent) in a quantitative field such as Computer Science, Mathematics, Physics, Statistics, Economics, Engineering, or a related discipline.
• 0-3 years of industry work experience, internships, academic research, thesis work, significant coursework or independent projects involving data analysis or machine learning
• Strong quantitative, analytical, and communication skills
• Proficiency in Python and familiarity with machine learning packages, with experience in other languages a plus
• Experience working with and analyzing data from multiple sources and in multiple formats
• Familiarity with machine learning concepts such as supervised learning, model evaluation, and feature engineering
• Interest in financial markets, intellectual curiosity, and comfort in working on open-ended problems
• Strong written and verbal communication skills

Current anticipated annual base salary range: $110,000- $145,000

Base salary within the range will be determined by various factors including but not limited to the individual's experience, skills and qualifications.

We ask that all application questions be completed without the use of AI or automated tools. Submissions should reflect your own original thinking and experience.