R&D Senior Data Domain and Product Owner

Purpose

As part of our broader digital transformation journey, Chiesi is spearheading the creation of a cutting-edge R&D Data Hub—an enterprise-wide platform designed not only to elevate data accessibility, interoperability, and governance across R&D domains, but also to accelerate scientific insight and unlock transformative value throughout the entire drug-development lifecycle. 
The (senior) Data domain and product owner will lead data knowledge management strategy and execution for the R&D Data Hub to maximize the value of R&D Data. This role is both strategic and hands-on:   
you will define the semantic and quality aspect of the R&D Data Pipeline through metadata definitions, Data Catalogues creation, ontologies, knowledge graph and data quality that make R&D data organized, discoverable and reliable.  
In collaboration with R&D functional SME and IT, you establish and enforce governance processes, ensuring that R&D Data Pipeline is consuming high quality Data to drive new insights. Your influence will span scientific, and IT teams as you champion a data-driven culture across R&D 

Main Responsibilities

•    Semantic & Knowledge Strategy:  

Lead the creation of semantic layers and ontologies that unify R&D data vocabularies. Architect a knowledge graph or semantic model that integrates heterogeneous R&D data via standardized ontologies 

Ensure metadata and business glossaries are linked and machine-readable, enabling advanced data discovery and AI-driven insights 

•    R&D MetaData and Standardization:  

Develop and implement a comprehensive R&D data governance framework. Define policies, standards and best practices for data stewardship (roles, ownership, quality rules). Work closely with functional SME (e.g. clinical, safety, CMC) to ensure data is defined, documented and maintained according to governance policies aligned with Chiesi Global guideline. 

Promote adoption of FAIR principles to make R&D data findable and reusable 

•    Data Product Ownership:  

Define product requirements, prioritize features, and manage the backlog for data assets delivered by the engineering team. Collaborate with engineers to oversee pipeline and schema changes so that outputs meet the needs of end users. 

Monitor product KPIs (data quality scores, usage metrics) and iterate on enhancements. 

•    Data Quality Management:  

Establish R&D data quality frameworks: set validation rules, quality metrics and automated monitoring to ensure integrity of critical data 

Integrate data quality checks (profiling, anomaly detection) into pipelines and alert stewards on exceptions. 

•    Stakeholder Engagement & Leadership:  

Communicate the value of data governance and semantic initiatives at all levels. Influence leaders by demonstrating how structured data and knowledge graphs improve research productivity and compliance 

Act as a mentor and ambassador, resolving conflicts and balancing technical and business priorities. 

•    Continuous Improvement:  

Establish metrics to track governance effectiveness (e.g. percentage of approved metadata, data quality improvements). Review and refine governance policies over time. Stay current on industry trends and recommend enhancements to processes and tools for continuous digital transformation of R&D.

Experience Required

Pharma & Data Expertise (7+ years): Proven experience in data management, governance, metadata & knowledge management ideally within pharmaceutical R&D or life sciences.  Deep knowledge of R&D data domains.  Experience with regulated environments (GxP/GDPR) is required.

Education

Advanced degree (Master’s/PhD) in Life Sciences, Data Science, Information Systems or equivalent experience. 

Languages

Fluent in English

Technical Skills

Technical Skills: 
•    Strong expertise in graph databases, semantic, and ontology platforms (e.g. SciBites, neo4j, snowflake). Experience with data governance and metadata tools (e.g. Collibra, Informatica, ...) 
•    Data Quality Management and data transformation (design and implementation of quality rules) 

Product Management Skills: 
•    Proven product owner mindset with experience in agile environments. Able to write product requirements, define roadmaps, and deliver data solutions iteratively. Strong analytical skills to translate complex research needs into governable data products 

IT skills     
•    Data Governance, science (incl AI and Generative AI), data management, knowledge of main system solutions relevant in Pharma R&D 

Soft Skills