Mgr Data Sciences - Information and Data Architecture

Job title: Mgr Data Sciences - Information and Data Architecture

About the Role The Mgr Data Sciences role focuses on developing interconnected business information and data architecture and ontologies that capture real-world meaning of data within pharmaceutical domains. The position enables semantic models and a connected data ecosystem across Operations (Process Development, Quality, Manufacturing, Engineering, Supply Chain) to empower decision-making and digital transformation.

What You'll Do

• Ensure successful delivery of connected data initiatives aligned with Operations Data Strategy roadmap.
• Lead conversations with business stakeholders to elucidate semantic models of pharmaceutical business concepts, aligned definitions, and relationships.
• Negotiate and debate across stakeholders to drive alignment and create system-independent information models, taking a data-centric approach aligned with business data domains.
• Design, develop and deploy a connected data ecosystem across Operations using Semantic modeling, R2RML, SPARQL, Generative AI, large language models, GraphRAG.
• Provide technical leadership in Python-based development, SQL-based data transformation, SPARQL retrieval, and AI system design.
• Develop comprehensive business information models and ontologies that capture industry-specific concepts, including CMC, Clinical, and Operations data.
• Facilitate whiteboarding sessions with business subject matter experts to elicit knowledge, drive interoperability across domains.

What We're Looking For

• Mid-level Data Sciences Manager with experience building interconnected data architectures and ontologies.
• Domain knowledge in pharmaceutical operations, including Process Development, Quality, Manufacturing, Engineering, and Supply Chain; familiarity with CMC and Clinical data.
• Strong ability to lead stakeholder discussions, align semantic models, and create system-independent information models.
• Proficiency in Python, SQL, SPARQL; experience with R2RML and graph-based retrieval and AI system design; familiarity with Generative AI and large language models (LLMs), GraphRAG.
• Experience in semantic modeling, ontology development, and building connected data ecosystems.

Nice to Have

• Experience with Generative AI, GraphRAG, LLMs, and Large Language Model pipelines.
• Knowledge of Structured Content Management and integrated data for regulatory submissions.
• Data governance, data lineage, and data quality practices.