Our Customer:
Our customer is a global life sciences organization that is building an enterprise-scale delivery capability for data products, digital automation and applied AI. The unit operates as an internal “delivery factory” that industrializes the path from idea to production across multiple global business domains, combining startup pace with enterprise standards for architecture, security and quality. This role supports the Pharma R&D domain within that delivery organization.
Your Tasks:
Own the product backlog and keep it prioritized, clear and aligned with approved business priorities
Translate business priorities into product scope, epics, features and user stories ready for delivery planning
Define acceptance criteria, assumptions and delivery readiness inputs for each feature
Identify execution dependencies, technical constraints, regulatory considerations and data readiness gaps at product level
Collaborate with CoEs, Architecture, Data and delivery teams to refine delivery-ready scope
Act as the primary delivery-facing product authority on feature scope, backlog priorities and acceptance decisions for ARTs, PODs and vendor teams
Ensure backlog items meet quality standards before PI and sprint planning
Drive product alignment with Enterprise Architecture, security and privacy, and data governance requirements
Translate production learnings, defects and enhancement requests into prioritized backlog actions
Partner with the business-facing Product Manager while owning backlog clarity and execution quality
Required Experience and Skills:
5+ years of experience as a Product Owner in Enterprise IT, combining business-facing product ownership with delivery execution
5+ years of experience with data, analytics, digital, automation or AI products in complex global organizations
5+ years of experience in backlog refinement and delivery readiness, including feature decomposition, backlog structuring and acceptance criteria definition
5+ years of experience in high-level technical assessment of data, digital and AI solutions, including dependencies and implementation implications
Strong understanding of end-to-end data and analytics lifecycles, including ingestion, modeling, transformation, consumption and operationalization of insights
Solid knowledge of data concepts such as data modeling, data integration and data quality for reporting, advanced analytics and AI use cases
Strong understanding of relational databases and querying concepts (SQL or similar technologies)
Broad understanding of enterprise cloud ecosystems, including productivity platforms, data platforms and cloud services for analytics and AI workloads
Hands-on experience with analytics, AI and ML-enabled solutions, including use-case shaping and model integration into business processes
Proven ability to understand existing systems, data landscapes, business processes and integrations, and turn business needs into scalable solutions
Hands-on experience supporting the design, implementation and adoption of business applications and workflow-based digital solutions
Strong focus on data quality assurance, including data definitions, quality checks and acceptance metrics across pipelines and analytical outputs
Proven ability to manage product-level trade-offs, priorities and dependencies within domain priorities
Strong stakeholder management and communication skills
Structured, detail-oriented working style with the ability to operate under ambiguity
Effective collaboration with engineers, architects and Scrum / ART roles to remove ambiguity and enable predictable delivery
English — Upper-Intermediate
Would Be a Plus:
3+ years of experience delivering or product-owning solutions in the pharma R&D business area
2+ years of experience delivering solutions in regulated or high-compliance environments with validation, audit readiness and controlled change management (e.g. GxP)
SAFe POPM, SAFe Practitioner or equivalent agile / produ
