Lead Applied Scientist

Thomson Reuters is seeking a Lead Applied Scientist. This is where emergent intelligence meets real‑world impact. You'll work at the cutting edge of GenAI and machine learning agents, building systems that combine neural and symbolic reasoning, understand multiple modalities, and personalize themselves to individual users and evolving contexts.

Your work will transform how millions of legal, tax, and compliance professionals interact with information—not through static tools, but through intelligent agents that understand their domain, anticipate their needs, and evolve with their workflows.

As a Lead Applied Scientist
, you will:

• Design and deploy neuro‑symbolic AI systems that seamlessly integrate neural learning with structured knowledge representation and ontologies
• Build multi‑modal models that synthesize insights across text, documents, structured data, and domain‑specific artifacts
• Explore the emergent intelligence at the intersection of GenAI and machine learning agents
  , creating autonomous systems that reason and act
• Develop adaptive domain‑specific reasoning capabilities that understand the nuances of legal, regulatory, and financial contexts
• Own and lead end‑to‑end research deliverables—from ideation and experimentation to production deployment
• Establish comprehensive AI evaluation frameworks, quality assessment methodologies, and observability systems that ensure reliability, fairness, and transparency
• Leverage advanced information retrieval techniques, prompting workflows, and model training strategies to optimize solutions
• Shape long‑term AI strategy by providing strategic input to business and Labs leadership
• Champion AI personalization and continual learning approaches that make products smarter with every interaction
• Lead stakeholder engagement across UX, Product, and Engineering teams to align AI capabilities with user needs
• Develop in‑depth knowledge of customer problems and data
• Mentor scientists and engineers on best practices in applied AI research and production ML systems
• Build expertise in knowledge representation, domain reasoning, and agentic AI architectures
• Foster a culture that balances scientific rigor with pragmatic delivery and customer value

You're a fit for the role Lead Applied Scientist if your background includes:

• Ph.D. in Computer Science, Machine Learning, AI, or related field (or Master’s with equivalent depth of experience)
• 7+ years building production IR/NLP systems for commercial applications with demonstrated business impact
• Strong software engineering capabilities with experience in production code, MLOps, and managed delivery pipelines
• Proven track record translating complex, ambiguous problems into successful AI applications
• Proficiency in AI evaluation, quality assessment, and observability for production systems
• Neuro‑symbolic AI architectures that combine learning and reasoning
• Expertise in multi‑modal modeling across diverse data types and representation formats
• Hands‑on experience with knowledge representation, ontologies, and semantic reasoning systems
• Track record implementing AI personalization and continual learning that adapts to user behavior and feedback
• Experience with adaptive domain‑specific reasoning in specialized professional contexts
• Familiarity with emergent intelligence at the intersection of GenAI and machine learning agents—including agentic frameworks, tool use, and autonomous decision‑making
• Demonstrated ability to scale impact through others in applied research or advanced development settings
• Outstanding communication skills—you translate complex technical concepts for diverse audiences
• Proven success collaborating with Product, Engineering, and Business stakeholders in agile environments
• Strong problem‑solving and analytical thinking with a bias toward action and iteration

• 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…