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.
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 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
Qualifications
Bachelor’s degree (or equivalent) in a rigorous quantitative field
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 (NumPy, SciPy, 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 evaluation and experimental design
Demonstrated ability to independently scope and execute open-ended technical projects
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.
