Senior Data Scientist

Your Role
Are you passionate about directly impacting the business by utilizing analytical and machine learning skills? Do you want to play a key role in designing and building autonomous AI agents, mining large datasets, developing advanced statistical and language models to generate meaningful and actionable insights, improve decision-making, optimize business processes, and help address business problems?
We are looking for a NLP / AI Data Scientist with 10+ years of experience:
• Contribute to end-to-end model and agent development, from prototyping through production deployment, adhering to best practices for software engineering and reproducible research
• Design, build, and iterate on autonomous agentic systems capable of multi-step reasoning, planning, tool use, and self-correction
• Develop and maintain LLM evaluation tooling to track model performance, reliability, and consistency across a variety of metrics throughout the development and deployment lifecycle
• Build LLM-powered applications over structured and tabular data, enabling natural-language querying, analysis, and insight generation on enterprise datasets
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines to ground agent outputs in real, verifiable data sources
• Work closely with data engineering and platform teams to ensure reliable, scalable, and production-ready data pipelines for AI systems
• Communicate technical concepts to both technical and non-technical audiences

Your team

You will be part of the Data Science team in the Data Analytics Foundational Platform Team (DAFP), WMA. Our Data Science team is at the heart of DAFP's function to manage and support data science efforts across different business areas. Our team is global, based in the US, Poland, and China.

Your expertise

Education
• Bachelor's degree or higher in Machine Learning, Computer Science, Computational Linguistics, Data Engineering, or other relevant technical field
Core AI & NLP Skills
• 15+ years of practical experience with techniques from natural language processing, machine learning, deep learning, and prompt engineering
• Demonstrated experience developing autonomous agentic systems, including multi-step task planning, tool integration, memory management, and feedback loops
• Hands-on experience building LLM applications on structured and tabular data — e.g., text-to-SQL, natural-language interfaces over databases, or LLM-driven analytics on enterprise datasets
• Experience implementing RAG architectures, including embedding generation, vector databases (e.g., FAISS, Pinecone, Weaviate, pgvector), and retrieval optimization

About us

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.

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. And we use artificial intelligence (AI) to work smarter and more efficiently. 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.