Mgr Data Sciences - Information and Data Architecture

ABOUT THE ROLE

You will play a key role in a regulatory submission content automation initiative which will modernize and digitize the regulatory submission process, positioning Amgen as a leader in regulatory innovation. The initiative leverages state-of-the-art technologies, including Generative AI, Structured Content Management, and integrated data to create a foundational knowledge repository for Operations Transformation prioritized initiatives.

Role Description:

The Mgr Data Sciences is a mid-level position responsible for developing interconnected business information and data architecture and ontologies that capture real-world meaning of data by studying the business, our data, and the industry. With a focus on pharmaceutical industry-specific data, including Operations domains such as Process Development, Quality, Manufacturing, Engineering and Supply Chain, this role involves creating robust semantic models based on data-centric principles to realize a connected data ecosystem that empowers consumers. The Information and Data Architect drives seamless cross-functional data interoperability, enables efficient decision-making, and supports digital transformation in pharmaceutical operations.

Roles Responsibilities:

• Lead and manage a team of Data Scientists (approximately 46 direct reports), providing mentorship, coaching, and performance management.
• Define team priorities, manage scope, and 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 pharmaceutical domains, and interface between data producers and consumers.
• Educate and mentor a team and peers on the practical use and differentiating value of Connected Data and FAIR+ data principles. Champion standards for master data reference data.
• Formalize data models in RDF as OWL and SHACL ontologies that interoperate with each other and with relevant industry standards like FHIR and IDMP for healthcare data exchange.
• Build a broad semantic knowledge graph that threads data together across end-to-end business processes and enables the transformation to data-centricity and new ways of working
• Guide the development of intelligent systems that combine structured and unstructured data, semantic models, and knowledge graphs.
• Apply pragmatic semantic abstraction to simplify diverse pharmaceutical and healthcare data patterns effectively.
• Drive adoption of FAIR data principles, data-centric design, and semantic interoperability across solutions.

Basic Qualifications and Experience:

[GCF Level 5A]

• Doctorate Degree OR
• Masters degree with 4-6 years of experience in Product Owner / Platform Owner / Service Owner OR
• Bachelors degree with 6-8 years of experience Product Owner / Platform Owner / Service Owner / Service Owner OR
• Diploma with 10-12 years of experience in Product Owner / Platform Owner / Service Owner

Functional Skills:

Must-Have Skills:

• Proven ability to lead and develop high-performing teams.
• Strong problem-solving, analytical, and critical thinking skills to address complex data challenges.
• Deep understanding of pharmaceutical industry data, including Process Development, Manufacturing, Engineering Quality, Supply Chain, and Operations.
• Advanced skills in semantic modeling, RDF, OWL, SHACL, and ontology development in TopBraid and/or Protg.
• Demonstrated experience creating knowledge graphs with semantic RDF technologies (e.g. Stardog) and testing models with real data.
• Highly proficient with RDF, SPARQL, Connected Data concepts, and interacting with triple stores.
• Highly proficient at facilitating, capturing, and organizing collaborative discussions through tools such as Lucidchart, and Confluence.
• Expertise in FAIR data principles and their application in healthcare and pharmaceutical data models.

Good-to-Have Skills:

• Experience in regulatory data modeling and compliance requirements in the pharmaceutical domain.
• Familiarity with pharmaceutical lifecycle data (PLM), including product development and regulatory submissions.
• Knowledge of supply chain and operations data modeling in the pharmaceutical industry.
• Proficiency in integrating data from various sources, such as LIMS, EDC systems, and MES.
• Hands-on data analysis and wrangling experience including SQL-based data transformation and solving integration challenges arising from differences in data structure, meaning, or terminology
• Expertise in FHIR data standards and their application in healthcare and pharmaceutical data models.

Soft Skills:

• Exceptional interpersonal, business analysis, facilitation, and communication skills.
• Ability to interpret complex regulatory and operational requirements into data models.
• Analytical thinking for problem-solving in a highly regulated environment.
• Adaptability to manage and prioritize multiple projects in a dynamic setting.
• Strong appreciation for customer- and user-centric product design thinking.