<p><b>Want a seat at the table where editorial content, product, and data intersect?</b></p><p><b>Can you bridge the gap between how content performs and how decisions get made?</b></p><h3>About the Team</h3><p>LexisNexis Legal &amp; Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of <a href="https://himalayas.app/companies/relx">RELX</a>, 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.</p><h3>About the Role </h3><p>The Content Performance Analyst is embedded in Global Editorial Operations (GEO) Strategy and focuses on content performance, content integrity, and quality measurement for AI-assisted production. You will unlock data across GEO and adjacent functions (Finance, Product, and Technology) to produce decision-ready insights and executive-ready narratives that inform prioritization and leadership decisions. You will also help strengthen the QA and measurement systems required for AI-assisted content production at scale, ensuring outputs across all content verticals are consistently high-quality, defensible, and improving over time. This role serves as a key enabler of data fluency within GEO, supporting QA, training, and adoption of performance insights across the organization.</p><p>Success in this role requires strong analytical discipline, sound judgment, responsiveness, and the ability to synthesize information quickly for senior audiences.</p><h3>Responsibilities </h3><h3>Data Management and Structuring</h3><ul><li><p>Execute recurring and ad hoc data pulls across editorial and operational systems</p></li></ul><ul><li><p>Reconcile inconsistencies across sources; validate, document and socialize metric definitions, assumptions, and calculation logic.</p></li></ul><ul><li><p>Structure datasets to align with specific strategic questions</p></li></ul><ul><li><p>Improve the efficiency and repeatability of recurring reporting processes</p></li></ul><ul><li><p>Align data practices with enterprise governance standards, including Global Operations (GO) data and analytics teams</p></li></ul><h3>Strategic Analytical Support</h3><ul><li><p>Translate leadership questions into measurable metrics and analytical plans; clearly articulate implications of findings, limitations and confidence levels (what’s proven vs. directional).</p></li></ul><ul><li><p>Conduct trend, variance, and performance driver analysis; identify where additional data or workflow instrumentation is needed to strengthen claims over time.</p></li></ul><ul><li><p>Support scenario comparisons