Senior Product Owner – Research & Ontology
Enhesa
| Company | Enhesa |
| Category | Product |
| Location | Lisbon |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 6 Jul 2026 |
| Last verified | 9 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Who We Are:
Enhesa is the leading provider of regulatory and sustainability intelligence worldwide. As a trusted partner, we empower the global business community with the insight to act today and prepare for tomorrow to create a more sustainable future - positively impacting our environment, our health, our safety, and our future. Navigating the fast-changing compliance and sustainability landscapes, we help them understand not just what they should do (first) but also how to do it. Both in their unique business and anywhere in the world. Now and in the future.
Our Mission:
Identify EHS requirements for the industry
Provide EHS compliance tools to companies
Advise companies in developing and implementing corporate EHS strategies
Enhesa’s core clients include Fortune 500 multinational companies. For more information, visit www.enhesa.com
As part of our highly dynamic team, we offer:
A competitive salary package & benefits with a flexible home-working policy
Work/life balance and a fast-paced and driven environment
Accountability and pride for your projects
Overview of the position
The Senior Product Owner (SPO) – Research & Ontology is responsible for strategically managing the development of AI research initiatives, semantic search capabilities, ontology and metadata frameworks, and AI-powered product features that support our Enhesa AI products. You will work closely with other product owners, product managers, engineers, researchers, ontology specialists, designers and other stakeholders to ensure that innovative ideas are validated and transformed into scalable solutions. You will have a deep understanding of the technical and functional aspects of the product; this means knowing how it works, what makes it unique, and how it fits into the larger picture of the user’s needs and company’s overall strategy.
Main tasks and responsibilities
Engaging with Product Managers, Engineering, Architecture, AI teams and business stakeholders to execute the vision, roadmap, and strategic direction of Enhesa’s AI research initiatives, ontology capabilities, and AI-powered products
Defining and prioritizing AI research initiatives, semantic search capabilities, ontology improvements, metadata enrichment strategies, and AI-powered customer experiences that support strategic AI initiatives across the portfolio.
Contributing to the definition and execution of the strategy, roadmap, and priorities for semantic search, retrieval-augmented generation (RAG), knowledge discovery, ontology evolution, metadata management, and AI-enabled content experiences.
Ensuring AI research initiatives, ontology models, metadata frameworks, and AI-powered functionality align with governance, security, privacy, regulatory, explainability, and customer requirements.
Working closely with Architecture, Engineering and AI teams to define system interactions, knowledge retrieval approaches, metadata standards, ontology structures, and AI solution capabilities.
Coordinate delivery across multiple products, ensuring dependencies, ownership transitions, and delivery handoffs are clearly defined and managed.
Facilitating the transition of initiatives from research, experimentation, and prototyping into product delivery, ensuring success criteria, business value, ownership, and delivery expectations are clearly understood before implementation begins.
Ensuring teams have sufficient customer context, business understanding, user workflows, prototypes, and solution objectives to make informed product and technical decisions throughout the product lifecycle.
Coordinating with architecture, engineering managers, tech leads, researchers, and engineers to ensure a common understanding of requirements, priorities, technical constraints, and business objectives.
Strategically prioritizing complex AI and ontology-related backlog items based on business value, customer impact, technical dependenci