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Thomson Reuters

Legal, financial data, and media services

Lead Applied Scientist - Document Understanding

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A PhD in Computer Science, AI, natural language processing, machine learning, information retrieval, or a related field, with demonstrable post-degree industry experience developing and deploying document understanding systems at scale.
  • Experience building production document understanding systems that go beyond basic optical character recognition or document parsing and deliver measurable business impact.
  • Hands-on depth across document analysis, information extraction, classification, knowledge representation, evaluation, and production deployment.
  • Have successfully taken advanced natural language processing and artificial intelligence capabilities from research through production and understand how to transform complex, unstructured content into structured knowledge that powers search, retrieval, reasoning, and intelligent workflows.

Responsibilities

  • 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 standard and customer-defined taxonomies.
  • Build large language model-based and traditional natural language processing 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, retrieval-augmented generation, and agentic artificial intelligence 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 artificial intelligence systems.
  • Provide technical leadership and input into artificial intelligence strategy, platform capabilities, and long-term roadmap decisions.
  • Mentor applied scientists and machine learning practitioners across the organization.

Desired Qualifications

  • Mentor others collaboratively and measure success by what ships and performs in production.
  • Understand how to transform large collections of unstructured documents into structured knowledge assets, build knowledge graphs from real-world content, use them to improve retrieval, reasoning, and artificial intelligence workflows, and extract meaningful insights from natural language and complex tabular content.

About the Company

Thomson Reuters

Legal, financial data, and media services

Thomson Reuters provides intelligent information and professional services for law, tax, finance, and media. It serves law firms, corporations, government agencies, and financial institutions with platforms like legal research (Westlaw), tax software (UltraTax CS), financial market data, and news services. These products are accessed through subscription-based platforms, ensuring ongoing updates and a steady revenue stream. The company also monetizes through advertising on media platforms and data analytics sales. It distinguishes itself by offering a broad, integrated suite of industry-specific tools combined with AI, cognitive computing, and machine learning to improve accuracy, efficiency, and predictive analytics in research and data insights. Its goal is to be a trusted partner that helps professionals make informed decisions by delivering reliable information and decision-ready tools while pursuing responsible, values-driven initiatives.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1851

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