Quantitative Engineer – GM Distribution Quant Analytics

UBS•New York, NY

2d•$300,000 - $335,000•Onsite

About The Position

We are looking for a Quantitative Engineer to join the Quant Analytics team in Global Markets Distribution. This role involves building analytics and AI solutions that directly influence Global Markets Distribution. The ideal candidate will be excited by solving real business problems with technology, turning ideas into production tools, and leading a team while remaining a hands-on engineer. You will own implementation, data quality, monitoring, cost, and releases. The role requires assessing data, selecting the right approach, building solutions, and putting them in front of users. You will design, build, and run analytics and AI services end-to-end, from data and models through to the tools people use. This position involves partnering directly with sales and trading desks, quants, and developers to frame problems and deliver working solutions. You will choose the right technique for each problem, reusing existing solutions where appropriate, and will be expected to raise engineering standards and help grow the team. This role is based in our New York City office.

Requirements

Four-year degree in mathematics, computer science, engineering, or a related field
Ideally 7+ years’ 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
Knowledge of Global Markets and sales workflows, or the ability to learn quickly by working closely with the desk
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, with sound judgment – validating outputs and aligning with policies, risk standards, and ethical use

Nice To Haves

Master’s degree
Experience with Kubernetes, Apache Airflow, Microsoft Azure, Apache Spark and kdb+/q

Responsibilities

Lead 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