GM Distribution - Quantitative Analyst/Engineer

Do you want to build analytics and AI solutions that directly influence Global Markets Distribution? Are you excited by solving real business problems with the right technology, whether that’s a simple statistical model or a modern LLM-based system? Do you enjoy turning ideas into production tools that win business and save people time? We are looking for a Quantitative Engineer to:

• 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 You’ll join the Quant Analytics team in Global Markets Distribution; a Centre of Excellence that works directly with the sales and trading desks we support. We’re a small, informal team working across AI, analytics, and engineering, with the freedom to use the approach that best solves each problem. You’ll have modern tooling within the bank’s governance framework, fast and direct feedback from the desk, and the opportunity to help shape the team as it grows.
• degree in mathematics, computer science, engineering, or a related field, with a master’s degree preferred
• 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, plus confidence working with cloud and containerized deployments
• experience with Kubernetes, Apache Airflow, Microsoft Azure, Apache Spark and kdb+/q is welcome but not required
• 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
• You’re curious to explore how AI can improve how we build, deliver, and optimize workflows. You do this with sound judgment – validating outputs and aligning with policies, risk standards, and ethical use UBS is a leading and truly global wealth manager and the leading universal bank in Switzerland. We also provide diversified asset management solutions and focused investment banking capabilities. Headquartered in Zurich, Switzerland, UBS is present in more than 50 markets around the globe. We know that great work is never done alone. That’s why we place collaboration at the heart of everything we do. Because together, we’re more than ourselves. Want to find out more? Visit ubs.com/careers .

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