GM Distribution - Quantitative Analyst/Engineer

UBS•New York, NY

2d•$233,750 - $251,250•Onsite

About The Position

GM Distribution is seeking a Quantitative Engineer to build analytics and AI solutions that directly influence Global Markets Distribution. This role involves solving real business problems with technology, from simple statistical models to modern LLM-based systems, and turning ideas into production tools. The position is based in New York City.

Requirements

4 year degree in mathematics, computer science, engineering, or a related field.
Experience as a team lead or manager while remaining a hands-on Quantitative Engineer, providing technical leadership, supporting team development, and delivering production solutions.
Strong Python skills and production engineering practices, including testing, version control, code review, and sound software architecture.
Hands-on experience with modern AI tooling, including LLM-based and agentic applications.
Solid data engineering experience with large, complex financial datasets.
Confidence working with cloud and containerized deployments.
Clear, concise communication, sound judgment, and a practical approach to solving unfamiliar business problems.
Curiosity to explore how AI can improve how we build, deliver, and optimize workflows, validating outputs and aligning with policies, risk standards, and ethical use.

Nice To Haves

Master's degree preferred.
Experience with Kubernetes, Apache Airflow, Microsoft Azure, Apache Spark and kdb+/q.
Knowledge of Global Markets and sales workflows, or the ability to learn quickly by working closely with the desk.

Responsibilities

Lead/work with a team while remaining a hands-on engineer, owning implementation, data quality, monitoring, cost, and releases.
Solve real Distribution problems by assessing the data, selecting the right approach, building solutions, and putting them in front of users.
Design, build and run analytics and AI services end to end, from data and models through to the tools people use.
Partner directly with sales and trading desks, quants, and developers to frame problems and deliver working solutions.
Choose the right technique for each problem, reusing existing solutions where appropriate.
Raise engineering standards and help grow the team as we continue to hire.