Sr. Manager Data Science - Gen AI and Content Systems

Would you like to lead a team building production‑grade NLP and LLM capabilities that transform legal and business information into actionable intelligence?

Do you enjoy shaping the future of AI and data science in a way that delivers measurable customer and business impact?

** Please note that the selected individual for this role will be expected to work in our Raleigh, NC location from the time of joining. If you reside outside of the Raleigh region and you are unable or unwilling to relocate, then please consider other roles across our organization that might allow for remote locations. **

Lexis Nexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (), a global provider of information‑based analytics and decision tools for professional and business customers. Our company has been a long‑time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi‑model approach that prioritizes using the best model from today’s top model creators for each individual legal use case.

The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles ().

At Lexis Nexis, we’re redefining legal intelligence through advanced AI. Our teams harness the power of large language models (LLMs), RAG, agentic systems and large‑scale structured and unstructured content systems to build tools that acquire, structure, enrich and deliver legal content. Join us in building AI systems that help legal professionals move faster, think deeper, and operate with confidence.

We are seeking a hands‑on Senior Manager of Data Science to lead a high‑impact team in developing the strategy, standards, and execution of AI across our content ecosystem. You will lead a team that embeds machine learning and generative AI directly into production systems operating at scale.

Our applied research opportunity balances innovation with practical constraints (e.g. latency, cost, reliability), requiring a strong ability to quickly iterate on prototypes (e.g. “vibe coding”), communicate tradeoffs, and rapidly deploy to production environments.

This role is central to our transformation toward an intelligent, agent‑enabled content platform which is capable of grounded reasoning, turning structured and unstructured data sources into legal knowledge.

• Set the vision and strategic priorities, acting as a recognized expert for Data Science
• Lead and develop a team of data scientists and ML engineers, setting the cultural tone for the group
• Drive applied research with a clear path to production, explicitly balancing innovation against real‑world constraints including latency, cost, and reliability
• Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human‑in‑the‑loop feedback systems to rigorously measure model quality and business impact
• Champion hands‑on rapid prototyping and iteration
• Collaborate with other Data Science teams to maximize re‑use of components and patterns, eliminating waste, duplication and unnecessary customization
• Operate with broad scope, coordinating across multiple cross‑functional teams, systems, and domains

• Collaborate closely with other Data Science teams, to define and execute the AI roadmap across the content lifecycle, maximizing reuse in areas including:
• Content collection (e.g. “web scraping”) and transformation
• Metadata extraction, enrichment, and classification
• Agentic workflows turning real‑world events and legal content into legal intelligence
• AI‑powered downstream product capabilities
• Design and deploy scalable, production‑grade AI systems, including:
• LLM‑powered document understanding and generation
• Agentic workflows balancing agent autonomy and efficiency with required structure and accuracy
• Retrieval‑augmented…