Senior Product Manager – Editor Decision Support

Overview

As Senior Product Manager, you own the vision, strategy and roadmap for editorial decision support in the post-peer-review stage. You work within a cross-functional squad to turn manual workflows into system-guided support, partnering with editors, data scientists and engineers to evaluate AI-enabled capabilities and impact. You’ll balance product and technical trade-offs while ensuring transparency and responsible use of AI. This role shapes how editors decide on manuscript outcomes across Elsevier’s editorial workflow, contributing to trusted publication. You join a mission-driven team modernising submission experience for researchers worldwide.

Pay / Benefits

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• flexible working hours
• country-specific benefits

Responsibilities

• Define and drive the product vision, strategy and roadmap for post-peer-review editorial decision-support
• Lead delivery within a cross-functional agile team and manage backlog
• Collaborate with scientific editors to extract insights and translate into product improvements
• Champion evidence-based decision-making through research, analytics and metrics
• Partner with data science to evaluate AI capabilities and prioritise enhancements
• Measure success with KPIs/OKRs and explore build/buy/partner options
• Balance product, technical and architectural considerations with engineering teams
• Ensure transparency, accountability and human oversight for AI usage
• Coordinate with Publishing, Technology, Operations, Editorial Strategy and Research Integrity for alignment
• Support go-to-market, launches, adoption and migration planning

Key requirements

• Significant product management experience with strategy, roadmap, and delivery ownership
• Experience with AI/ML products including generative AI, RAG or agent-based workflows
• Strong analytical skills with data-driven decision-making in evolving environments
• Experience leading cross-functional initiatives and translating model evaluations into product decisions
• Ability to understand technical concepts and work with engineering teams
• Excellent communication and stakeholder management
• Commercial awareness and understanding of how product decisions drive customer value
• Familiarity with scholarly publishing or editorial workflows is advantageous

• strong collaboration
• stakeholder management
• analytical mindset
• AI/ML product experience
• experience with evaluation metrics (qualitative/quantitative)
• prototyping with AI tools (helpful)

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