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LexisNexisVerified from LexisNexis' careers page · Workday

Patent Analytics Expert (IP & Portfolio Analysis Products)

  • Farringdon
  • 1 Years
  • Posted Nov 6, 2026

At a glance

Patent Analytics Expert (IP & Portfolio Analysis Products) at LexisNexis, based in Farringdon. Experience level: 1 Years. Posted on 6 November 2026, read from LexisNexis' own careers page (Workday), which RealAnalystJobs re-checks daily and removes the role when it closes.

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About this role

Patent Analytics Expert (IP & Portfolio Analysis Products) Do you want your patent analytics expertise to shape the products and AI that others rely on? Do you revel in testing a polished AI answer and finding exactly where its reasoning breaks down? Come join us and help build the world's most trusted patent analytics and AI for IP and business decision-makers! About the team: LexisNexis Intellectual Property, part of RELX, helps the world's innovators, IP professionals, law firms, and investors understand patents: their own, their competitors', and those of the companies they are assessing. Our products, including PatentSight+, IPlytics and TotalPatent One, and the patent data we deliver directly into customers' own systems, inform decisions on R&D investment, licensing, litigation, M&A and competitive strategy. Many of our customers use patents to make business decisions: comparing their portfolio with competitors', spotting acquisition or licensing targets, deciding what to keep or drop, or tracking where a technology is heading. Increasingly, they ask an AI assistant to help. We're looking for someone who knows what good answers to these questions look like, to help make sure our products, and the AI inside them, give them. About the role: "How does our portfolio compare with our three closest competitors?" "Which companies would strengthen our position in power electronics?" "What does this patent actually claim?" Answering questions like these well takes judgment: which companies to group together, how to count, which time window to use, how to read a claim, and what the numbers can and cannot support. AI can now produce a polished answer in seconds, which makes that judgment more important, because a convincing answer can still be a wrong one. In this role, you will represent the people who ask these questions, and you will be forward deployed: working closely with the product and engineering teams building specific end-user experiences, rather than advising from a distance. You will define what a genuinely useful answer looks like across different use cases, contexts, and data scenarios, and encode that judgment in artefacts and other mechanisms, including AI agents, so it can scale. You will test our analytics and AI against it and help shape what we build next. Key Responsibilities: • Be the voice, alongside the product and engineering teams building our end-user experiences, of the various types of end users who use patent analytics for business decisions: IP and portfolio managers, competitive-intelligence analysts, corporate development and M&A teams, R&D strategists and investors, by deeply understanding their needs, aspirations and the jobs they need to get done. • Define what good looks like across a broad range of AI-assisted uses, such as portfolio exploration and comparison, competitor benchmarking, technology and sector trends, acquisition and licensing target identification, portfolio optimisation, and patent summaries and claim interpretation. • Capture that definition in governed, versioned question sets, graded example answers, scoring rubrics, and acceptance criteria that teams build closed-loop evaluations on, and work closely with engineering teams to guide them as they build evaluation frameworks on these. • Perform structured human-in-the-loop evaluations and drive the eyes-on evaluation layer for our AI assistant and analytics features: design and carry out expert reviews, judge whether results meet the bar, and feed what you learn back into the standards. • Collaborate closely with other patent experts in the team to develop methods that scale eyes-on human evaluation. • Help engineering teams troubleshoot issues by working backwards from AI outputs to the underlying data sources, to identify root causes. • Help shape new AI use cases and features: spot questions customers cannot easily answer today, and test ideas with customers and our user research colleagues. • Scope and lead your own projects independently, from defining the problem to sharing recommendations with senior stakeholders. • Track how competitors and new AI tools serve these users, and flag opportunities and risks. • Help capture your judgment in tools and AI assistants that let others apply it at scale and evaluate those tools over time to detect drift and develop ways to recalibrate them. Requirements: • Experience using patent data to answer business questions, for example in patent analytics, IP or innovation consulting, competitive intelligence, portfolio management, IP due diligence, or technology scouting. • A strong technical background, enough to read patents critically and interpret what their claims cover. • A solid understanding of patent data and its pitfalls for analysis, such as family counting, ownership roll-ups, legal status, citations, classification, and time effects • The ability to explain clearly why an analysis is or is not trustworthy, and what would make it better. • Hands-on analytical skills, for example with analytics platforms, Excel, SQL, or Python • Practical use of AI tools in your own analytical work beyond basic chat, including effective prompting, and a clear view of where they help and where they mislead. • Confidence working independently and presenting recommendations to senior, non-specialist audiences. • Curiosity about technology, industries and how decision-makers actually use what you give them. Experience and education. • Typically, 3 or more years in patent analytics, competitive intelligence, IP consulting, portfolio management, or related roles; we will also consider candidates earlier in their careers who show strong promise. • A degree in a scientific, engineering, or technical subject, or equivalent practical experience; a further degree is a plus. It would be great if you also have. • Hands-on experience with patent analytics platforms • Experience with AI evaluation, for example rubric-based scoring, LLM-as-a-judge, or h

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