Patent Analytics Expert (IP & Portfolio Analysis Products)

RELX

3d•€59,300 - €98,600

About The Position

LexisNexis Intellectual Property is seeking a patent analytics expert to represent end users and help shape its patent analytics products and AI. The role works closely with product and engineering teams building end-user experiences, defining what useful and trustworthy answers look like across varied use cases, contexts, and data scenarios. The expert’s judgment will be captured in governed evaluation materials and tools, including AI agents, so it can be applied consistently and at scale. The role covers AI-assisted portfolio exploration and comparison, competitor benchmarking, technology and sector trends, acquisition and licensing target identification, portfolio optimization, and patent summaries and claim interpretation. The successful candidate will test analytics and AI features, conduct structured human evaluations, troubleshoot issues by tracing outputs to underlying data, identify new customer use cases, and lead independent projects through recommendations to senior stakeholders. The position also monitors competitors and new AI tools and helps evaluate tools over time to detect drift and recalibrate them.

Requirements

Experience using patent data to answer business questions, such as in patent analytics, IP or innovation consulting, competitive intelligence, portfolio management, IP due diligence, or technology scouting.
A strong technical background sufficient to critically read patents and interpret the scope of their claims.
Understanding of patent data and analytical pitfalls, including family counting, ownership roll-ups, legal status, citations, classification, and time effects.
Ability to explain clearly why an analysis is or is not trustworthy and what would improve it.
Hands-on analytical skills, for example using analytics platforms, Excel, SQL, or Python.
Practical use of AI tools in analytical work beyond basic chat, including effective prompting, and a clear understanding of where AI helps and can mislead.
Confidence working independently and presenting recommendations to senior, non-specialist audiences.
Curiosity about technology, industries, and how decision-makers use analytical outputs.
Typically, at least 3 years of experience in patent analytics, competitive intelligence, IP consulting, portfolio management, or related roles; candidates earlier in their careers who show strong promise will also be considered.
A degree in a scientific, engineering, or technical subject, or equivalent practical experience.

Nice To Haves

A further degree.
Hands-on experience with patent analytics platforms.
Experience with AI evaluation, such as rubric-based scoring, LLM-as-a-judge, or human-in-the-loop review.
Experience with M&A, IP due diligence, licensing, or portfolio valuation.
Experience with prior-art or freedom-to-operate work.
A background in a technology-intensive industry or R&D.
Experience working with product teams or user researchers.

Responsibilities

Represent the needs of patent-analytics end users, including IP and portfolio managers, competitive-intelligence analysts, corporate development and M&A teams, R&D strategists, and investors.
Define quality standards for AI-assisted portfolio exploration and comparison, competitor benchmarking, technology and sector trends, acquisition and licensing target identification, portfolio optimization, patent summaries, and claim interpretation.
Create governed, versioned question sets, graded example answers, scoring rubrics, and acceptance criteria for closed-loop evaluations.
Work with engineering teams as they build evaluation frameworks.
Design and conduct structured human-in-the-loop evaluations of the AI assistant and analytics features, judge whether results meet the required standard, and update standards based on findings.
Collaborate with patent experts to develop scalable methods for human evaluation.
Help engineering teams troubleshoot by tracing AI outputs to underlying data sources and identifying root causes.
Identify customer questions that current products do not answer easily, and test potential AI use cases and features with customers and user research colleagues.
Independently scope and lead projects from problem definition through recommendations to senior stakeholders.
Track competitors and new AI tools, and identify opportunities and risks.
Capture expert judgment in tools and AI assistants so others can apply it at scale, and evaluate those tools over time to detect drift and develop recalibration methods.