Lead Applied Scientist, Document Understanding
AI in this role
Lead Applied Scientist, Document Understanding
This position is based in either Zug, Switzerland or London, UK.
Want to use your experience of building document understanding AI solutions to enhance our leading products in the tax, legal and professional services industries?
Document understanding is a foundational intelligence layer that powers every major capability across our legal AI platform—from search and information extraction to agentic reasoning in products like Westlaw, PracticalLaw, and CoCounsel. You'll build state-of-the-art semantic chunking, document enrichment, and knowledge graph construction systems that serve as the cognitive foundation multiple product teams depend on, working across authoritative legal, tax, and accounting content and extraordinarily diverse customer data.
This is a rare opportunity to solve publishing-quality research problems with immediate production impact—your innovations will directly shape how millions of legal professionals research, analyze, and reason over complex legal documents while advancing the capabilities that enable the next generation of intelligent legal AI agents.
You will work across semantic chunking, document enrichment, intelligent classification, information extraction, knowledge graph construction, and tabular data understanding for complex legal, tax, and accounting content. This work forms the foundation for downstream search, retrieval, reasoning, and generative AI experiences.
About the Role
As Lead Applied Scientist, Document Understanding at Thomson Reuters, you will:
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
You're a fit for the role of Lead Applied Scientist, Document Understanding if you have:
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.
#LI-JB2
What’s in it For You?
- Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.
- Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
- Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
- Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
- Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
- Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
- Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.
DISCLAIMER
The above information in this description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required of employees assigned to this job.
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.
As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.
We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. More information on requesting an accommodation here.
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More information about Thomson Reuters can be found on thomsonreuters.com
How we rate this
Lead Applied Scientist, Document Understanding at Thomson Reuters rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, AI Agents, and NLP. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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