R&D Data Management Expert - Data and AI

Job highlights

• MS in Biological Sciences or MS Computer Science,Data Science,and Information Management preferably in a biomedical context. . 10+ years in data catalog,metadata management and controlled vocabularies,related data and search capabilities in the biomedical domain

Job Posting Title

R&D Data Management Expert - Data and AI

Job Description Summary

Develops, implements, and administers policies and procedures to ensure the quality and protection of organizational data, aligning with strategic priorities and regulatory requirements to minimize risk. Collaborates with leadership to define specific roles and responsibilities related to the organizations data assets, ensuring clarity in data stewardship. Develops and maintains rules and policies governing data management and access restrictions, promoting best practices in data governance. By fostering a culture of compliance and accountability, plays a critical role in safeguarding the organizations data integrity and security.

Purpose

Insight generation and business process optimization across R&D by ensuring internally generated data as well as externally sourced data are FAIR+Q (Findable, Accessible, Interoperable, Reusable, and of Quality). This is achieved, among other things, through the development and implementation of key data capabilities:

• Data and AI Catalog: Central location to find data assets, data models, AI Models, applications and related governance information
• Business Information Modeling: Connecting and describing data across domains
• Reference Data Capability: Providing standards for use in information models and workflows.
• Business Data Architecture: Charting data flow across business processes and systems
• Master Data Capability: Maintaining authoritative sources of key business data concepts such as project, product, sites etc. to gain a trusted, current view of these key data

The role will be responsible for the vision and delivery of the Data and AI Cataloging Capability while closely collaborating with Business Information Modeling, Business Data Architecture, Reference Data Standards, and other relevant data and AI capabilities. The domain scope of the role is all of R&D. The Data and AI Catalog expert acts as a business leader in initiatives involving the cataloguing of data, data models, AI models, governance policies and related information to allow humans and AI Agents access to information that is relevant for business workflows. The role collaborates closely with stakeholders across the organization to strengthen discoverability, understanding, ownership, quality, and usage conditions of R&D data assets.

Stakeholders

The role will interact and establish strong partnerships with

• Data producers, consumers, owners and stewards across R&D. These could include lab and computational scientists, clinicians, statisticians, pharmaceutical and regulatory affairs professionals.
• Data architects, engineers, UI/UX engineers, product/platform/technology teams
• AI and ML scientists, engineers etc.
• Vendors and external contract staff
• Relevant Industry forums / consortia in the data and information space.

Roles and Responsibilities

Data and AI Catalog Management (Pharma R&D Focus): Co-create and own the product vision, strategy, roadmap, rollout (resourcing, budgeting and delivery) and adoption (communication and metrics) of an R&D data and AI Model Catalog capability that aligns with the broader goals of the Pharmaceutical R&D team. This includes cross-domain search and discovery capabilities that enable scientists to find relevant data assets and reduce time to insight. Translate data cataloguing into business value by driving alignment between R&D data and AI model Catalog and key R&D business outcomes. Implement and manage specialized R&D data and AI Model catalog/s to maintain an inventory of a vast array of scientific data assets, from raw instrument outputs to curated datasets, data products, data models, AI models and services and governance policies.

• Partner with business and technical teams to create cohesive frameworks for access control, modeling, cross-domain discoverability, reusability for various analytics and AI use cases, and lifecycle management
• Enhance the catalog by ensuring integration with R&D systems, processes and AI services allowing scientists to quickly grasp the meaning, context, and reliability of data. Liaise with IT to troubl…