Overview

Job Reference # 324698BR

City London

Job Type Full Time

Your role

• Be part of a small team that designs and implements analytical tools to bring enterprise statistical computing to sell-side equity research
• Contribute technically and creatively to the development of a shared ecosystem enabling stock analysts and other stakeholders to build, use, and share reproducible, scalable, and collaborative data analysis workflows
• Help drive the development of LLM-powered tools and Agentic AI workflows to support hypothesis-driven empirical research
• Propose, design, maintain, and support core utilities upholding a ‘live data analysis’ paradigm, enabling dynamic, real-time collaboration across diverse stock research and data analysis teams
• Build and maintain applications that make hypothesis-driven empirical research accessible and actionable for stock analysts
• Provide one-on-one coaching and run workshops to train and orient investment professionals on the use of Global Research’s enterprise statistical computing platform (built on Python) for managing and analysing data as part of their approach to stock analysis
• Collaborate with data and technology partners across the firm to integrate research workflows with enterprise systems

Your team

You will be working in the Empirical Scientific Approaches (Global Research) team. We are bringing hypothesis-driven empirical methods and enterprise statistical computing practices to sell-side equity research. This is an opportunity to be part of a dynamic, creative, globally distributed team positioned at the centre of one of the top equity research departments in the world. As a Data Scientist/Engineer, you will be helping to establish the next frontier of sell-side equity research.

Your expertise

• Proven experience applying software development principles (e.g., modularity, reproducibility, testing) to empirical research workflows
• Proven track record of successful development of applications using an object-oriented programming paradigm, preferably in Python
• Strong foundation in statistical modelling, causal inference, or machine learning
• Intellectual curiosity and excellent communication skills, with a track record of working effectively with technical and non-technical audiences
• Formal training in empirical methods, preferably within an empirical social science discipline (e.g., economics, quantitative sociology, statistics). In exceptional circumstances, equivalent professional experience with on-the-job training can substitute for educational credentials.
• Hands-on experience designing or deploying LLM-based systems (e.g. retrieval-augmented generation, prompt engineering, or evaluation frameworks) would be a major plus

About us

UBS is the world’s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.

We have a presence in all major financial centers in more than 50 countries.

Join us

At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.

We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.

Disclaimer / Policy statements

UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.