Lead Applied Scientist, Document Understanding

About Us

Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news. We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.

About the Role

Design and deploy semantic chunking systems for lengthy, non-uniformly structured legal, tax, and accounting documents Build document enrichment pipelines that identify document types, jurisdictions, legal concepts, entities, parties, and other domain-specific metadata Develop hierarchical and multi-label document classification systems using both standard and customer-defined taxonomies Build LLM-based and traditional NLP information extraction pipelines that identify entities, relationships, citations, references, and key concepts from unstructured content Develop knowledge graph construction systems that extract, normalize, connect, and enrich entities, legal concepts, citations, and relationships across large document collections Design systems that identify, interpret, and extract insights from complex tabular data embedded within legal, tax, regulatory, and accounting documents Create document intelligence capabilities that support downstream search, retrieval, RAG, and agentic AI workflows Design robust evaluation frameworks for document understanding systems using expert annotations, synthetic datasets, and production metrics Lead technical decisions on document analysis architectures, chunking strategies, extraction methodologies, classification approaches, and knowledge representation frameworks Partner closely with engineering teams to deliver scalable, reliable, and production-ready AI systems Provide technical leadership and input into AI strategy, platform capabilities, and long-term roadmap decisions Mentor applied scientists and machine learning practitioners across the organization

About You

PhD in Computer Science, AI, NLP, Machine Learning, Information Retrieval, or a related field, with demonstrable post-degree industry experience developing and deploying document understanding systems at scale. Hands-on depth across document analysis, information extraction, classification, knowledge representation, evaluation, and production deployment. Successfully taken advanced NLP and AI capabilities from research through production and understand how to transform complex, unstructured content into structured knowledge that powers search, retrieval, reasoning, and intelligent workflows. A collaborative mindset where you can mentor and measure success by what ships and performs in production. Experience of building production document understanding systems that go beyond basic OCR or document parsing and deliver measurable business impact. An understanding of how to transform large collections of unstructured documents into structured knowledge assets, building knowledge graphs from real-world content and using them to improve retrieval, reasoning, and AI workflows and extracting meaningful insights from both natural language and complex tabular content.

Fähigkeiten

ML / AIPythonMachine Learning

Gehaltsspanne

Auf Anfrage

Dieser Arbeitgeber nennt die Gehaltsspanne im ersten Gespräch.

Kontakt