# Senior Product Manager - Editor Decision Support at RELX

AI Level 3, AI centrality 66 out of 100. London Wall.

## Details

- Company: [RELX](https://jobsbylevel.com/companies/relx)
- AI level: AI Level 3 (score 66 out of 100)
- Location: London Wall
- Salary: €100k-€167k
- Posted: October 7, 2026
- Apply: https://jobsbylevel.com/go/345e02f9-b75a-420e-ad50-bb9f6f25fd45

## Description

Are you passionate about building AI-enabled products that help editors make better, faster, and more transparent decisions? Do you enjoy working at the intersection of product strategy, scientific publishing, data science, and technology to deliver meaningful impact for global research communities? About our team Our team supports the editorial workflows of the largest scientific publisher in the world — millions of manuscript submissions a year, a portfolio of thousands of journals, and tens of thousands of editors. As submission volume grows, we modernise editorial processes, so they scale without compromising the integrity of the scientific record. We are a cross-functional, evidence-led team of engineers, UX, data scientists, product analysts and subject-matter experts, and we work to a simple standard: everything we do is grounded in user needs, and we use evidence to make commercially driven priority decisions. About the role This role owns a specific, high-value challenge: deep evaluation after peer review — the stage where an editor reads the reviewer reports and decides to accept, reject, or request revision, and ensure the manuscript reaches the publication standard. It is the most time-intensive stage in the workflow and the peak of editorial cognitive load and perceived editorial risk. We are looking for a Senior Product Manager with deep product judgement to own the strategy, vision and roadmap for editorial decision support at the post-peer-review stage. As a product manager for Elsevier’s global Submissions, you will enable growth of knowledge, research and innovation for our researcher community. You will work within the wider publishing experience. You will help define the path from today’s manual workflow through system-guided support. You will work closely with an embedded data science team to commission and interpret evaluations of decision-support response accuracy, relevance and trust, and partner with the teams that own peer-review analytics and the wider Editor experience pipeline. We’re looking for a Product Manager who stays close to the technology and can reason about how the AI behaves, how it is evaluated, and where the trade-offs lie — while remaining, first and foremost, a product manager rather than an engineer or data scientists. You will be part of a squad of engineers, UX, subject-matter experts and analysts to create a state-of-the-art user experience for article publication. Strong collaboration with stakeholders in Technology, Operations and Publishing will grow trust & build alignment. Creating solutions that are data-driven will contribute to our innovative culture for our digital submission products and services. You will inspire authors to return to Elsevier for the best experience from submission to trusted publication. Role Responsibilities Owning, defining and driving the product vision, strategy, and roadmap for editorial decision-support solutions in the post-peer-review process. Leading delivery within a cross-functional agile team, managing priorities, maintaining a healthy backlog, and ensuring consistent product delivery. Partnering with scientific editors to understand decision-making challenges, gather user insights, and translate findings into impactful product improvements. Championing evidence-based decision-making through qualitative and quantitative research, analytics, experimentation, and clear success metrics. Collaborating with data science teams to evaluate AI-driven capabilities, including review synthesis, contradiction detection, and evidence linking, and using findings to prioritise enhancements. Measuring product success through business and user outcomes, defining KPIs and OKRs, communicating value, and assessing build, buy, or partner options. Working with engineering and technical teams to balance product, technical, and architectural considerations. Ensuring products support transparency, accountability, and responsible use of AI, with appropriate human

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