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: 

• Advanced Degree in a quantitative field such as Computer Science, Machine Learning, Mathematics, Statistics or a related Engineering Degree (PhD preferred).
• Minimum of 1-3 years of experience in building and deploying models, developing algorithms for machine learning problems and analyzing data.
• Strong quantitative, analytical, and communication skills
• Experience in Python and familiarity with machine learning packages
• Experience in manipulating and analyzing data from varying sources
• Experience in time series analysis is a plus
• Solution-oriented and intellectually curious with ability to tackle open-ended problems