GM Distribution - Quantitative Analyst/Engineer

As a Quantitative Engineer at UBS, you will lead and contribute hands-on to build analytics and AI solutions for Global Markets Distribution. You will solve real desk problems by turning data into production tools and end-to-end services, collaborating with sales, quants, and developers. You’ll shape the approach for each problem, ensuring quality, scalability, and risk-aware deployments. This role blends leadership with hands-on engineering in a Centre of Excellence focused on AI, analytics, and market-driven solutions.

• flexible working options
• inclusive culture
• opportunity to work with AI and cutting-edge analytics
• professional growth and team-building
• supportive leadership
• disability inclusion and accommodations

• Lead and contribute as a hands-on engineer, owning implementation, data quality, monitoring, cost, and releases
• Solve distribution problems by assessing data, selecting approaches, and delivering user-facing solutions
• Design, build and run analytics and AI services end-to-end from data/models to tools
• Partner with sales and trading desks, quants, and developers to frame problems and deliver working solutions
• Choose appropriate techniques and reuse existing solutions where possible
• Raise engineering standards and contribute to team growth and hiring efforts

• Degree in mathematics, computer science, engineering, or related field; master's preferred
• Experience as a team lead or manager while remaining hands-on
• Strong Python skills and production engineering practices (testing, version control, code reviews, solid software architecture)
• Hands-on experience with modern AI tooling, including LLM-based and agentic applications
• Solid data engineering experience with large, complex financial datasets; comfortable with cloud and containerized deployments
• Experience with Kubernetes, Apache Airflow, Microsoft Azure, Apache Spark and knowledge of kdb+/q is welcome but not required
• Knowledge of Global Markets and sales workflows or ability to learn quickly by working with the desk
• Clear, concise communication, sound judgment, practical problem-solving approach
• Curiosity about how AI can improve workflows with risk-aware, policy-aligned outputs

• Clear communication
• Sound judgment
• Practical problem-solving
• Python
• Production engineering practices
• AI tooling including LLMs