Lead Applied Scientist, Document Understanding. This position is based in Zug, Switzerland or London, United Kingdom. You will apply your experience building document understanding AI solutions to enhance Thomson Reuters' products in the legal, tax, and professional services industries. Document understanding serves as a foundational intelligence layer powering search, information extraction, and agentic reasoning across our legal AI platform, including Westlaw, Practical Law, and CoCounsel. You will design and deploy semantic chunking systems for lengthy, non-uniformly structured legal, tax, and accounting documents; build document enrichment pipelines to identify document types, jurisdictions, entities, and other domain-specific metadata; develop hierarchical and multi-label classification; create LLM-based and traditional NLP information extraction pipelines; construct knowledge graphs that connect entities and concepts across large document collections; extract insights from complex tabular data embedded in legal, tax, regulatory, and accounting documents; and create document intelligence capabilities to support downstream search, retrieval, RAG, and agentic AI workflows. You will design robust evaluation frameworks, lead technical decisions on document analysis architectures and extraction methodologies, and partner with engineering teams to deliver scalable, production-ready AI systems. You will provide technical leadership and input into AI strategy and long-term roadmap decisions and mentor applied scientists across the organization. Qualifications include a PhD in

Lead Applied Scientist, Document Understanding. This position is based in Zug, Switzerland or London, United Kingdom. You will apply your experience building document understanding AI solutions to enhance Thomson Reuters' products in the legal, tax, and professional services industries. Document understanding serves as a foundational intelligence layer powering search, information extraction, and agentic reasoning across our legal AI platform, including Westlaw, Practical Law, and CoCounsel. You will design and deploy semantic chunking systems for lengthy, non-uniformly structured legal, tax, and accounting documents; build document enrichment pipelines to identify document types, jurisdictions, entities, and other domain-specific metadata; develop hierarchical and multi-label classification; create LLM-based and traditional NLP information extraction pipelines; construct knowledge graphs that connect entities and concepts across large document collections; extract insights from complex tabular data embedded in legal, tax, regulatory, and accounting documents; and create document intelligence capabilities to support downstream search, retrieval, RAG, and agentic AI workflows. You will design robust evaluation frameworks, lead technical decisions on document analysis architectures and extraction methodologies, and partner with engineering teams to deliver scalable, production-ready AI systems. You will provide technical leadership and input into AI strategy and long-term roadmap decisions and mentor applied scientists across the organization. Qualifications include a PhD in