$ cat jobs/sr-manager-data-science-gen-ai-and-content-systems-relx-14c8c51ef4d2.json
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? ***US Based Remote Opportunity*** About our Team : LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), 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 ( About LexisNexis Data Sciences: At LexisNexis, 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. Role Overview: 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
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