JOB DESCRIPTION

The Data Science team plays a crucial role in supporting a variety of investment teams and internal stakeholders, addressing operational requirements, and contributing to alpha creation for portfolio managers. Comprised of individuals with a deep understanding of the markets, our team brings together diverse expertise spanning statistics, applied mathematics, machine learning, quantitative finance, and computer science. This role presents an exceptional growth opportunity, given the interdisciplinary nature of the team and its wide-ranging responsibilities covering both systematic and discretionary trading styles across major asset classes.

The successful candidate will find numerous ways to assist portfolio managers, leading to a considerable range of daily activities, often driven by specific requests from investment groups. We seek candidates who are entrepreneurial, capable of thinking creatively about leveraging existing or future technology and data assets to address business challenges. We value a balance between a willingness to embrace mathematical complexity when necessary and the ability to simplify solutions as needed. Tasks may vary from applying advanced statistical techniques to implementing clever workflow optimizations. Proficiency in rapidly prototyping, testing, and deploying analytic pipelines and models, particularly within the Python ecosystem, is essential. Additionally, candidates should possess strong interpersonal skills to cultivate relationships with internal clients.

Responsibilities:

• Cultivate relationships with systematic investment teams, comprehend their workflows, and propose enhancements.
• Conduct tailored statistical studies in response to specific requests from portfolio managers and internal clients.
• Deploy analytical tools across the organization, leveraging tools from applied mathematics, statistics, and machine learning, and utilizing appropriate technologies.
• Contribute to strategic quantitative initiatives focused on building innovative analytical infrastructure and services, enhancing competitive advantages for portfolio managers at ExodusPoint compared to other platforms.

Qualifications:

• Prior experience in front-office quantitative roles or activities related to alpha generation.
• Educational background in statistics, physics, mathematics, computer science, machine learning, or other quantitative fields, preferably at the Ph.D. level, with evidence of research accomplishments.
• Proficiency in software development, demonstrated through open-source contributions or equivalent.
• Proven track record of business acumen and creativity, showcasing the ability to execute innovative ideas in a technical manner.