R&D Business Data Expert DSAI

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where ,Health for all, Hunger for none' is no longer a dream, but a real possibility. We're doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining 'impossible'. There are so many reasons to join us. If you're hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there's only one choice.

R&D Business Data Expert DSAI 

POSITION 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 through the development and implementation of key data capabilities:

1. 
   
2. Business Information Modeling: Connecting and describing data across domains
3. Business Data Architecture: Charting data flow across business processes and systems
4. Reference Data Capability: Providing standards for use in information models and workflows.
5. Data Catalog: Central location to find data assets, data models and related governance information

The R&D Data Expert role will be responsible for the vision and delivery of the Business Information Modeling and Business Data Architecture capabilities while closely collaborating with Reference Data Standards, and Data Catalog.  The domain scope of the role is R&D with an initial focus on Research and Preclinical.

YOUR TASKS AND RESPONSIBILITIES:

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2. Business Information Modelling:

• Define and evolve fit-for purpose conceptual, logical, and physical data models in relational, graph-based or other formats that balance design and implementation, with standardization, scalability, and convergence with user-specific needs for customization and ease of use
• Ensure models support emerging scientific approaches, data types and AI/ML use cases and align with regulatory requirements (e.g., FDA 21 CFR Part 11, ICH GCP, GxP etc.).
• Ensure metadata capture of R&D Data assets to align with data models
• Own roadmap, rollout (resourcing, budgeting and delivery) and adoption (communication and metrics) of the business information modeling capability 

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2. Business Data Architecture:

• Define a vision and methodology for documenting data flows for prioritized use cases.
• Map data flow from production to consumption across business processes and system document attributes, formats, standards, transformations, user groups, system interactions etc. as part of the data flow
• Scale the data flow methodology across use cases to create a living document of R&D data flows
• Own roadmap, rollout (resourcing, budgeting and delivery) and adoption (communication and metrics) of the business data architecture capability

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2. Reference Data Standards & Data Catalog

• Partner with teams managing reference data standards, metadata, and data catalog tools for seamless operations across capabilities.
• Align modeling and architecture efforts with governance frameworks

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2. Strategic Leadership

• Influence without authority
• Operate collaboratively in a flat-hierarchy environment
• Advocate data capabilities usage across diverse R&D functions.
• Co-shape DSAI strategy for data and data science

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2. Working Relations:

The role will interact and establish strong partnerships with -

• Data producers, consumers, owners and stewards in Research Biology and Translational Science (RED CVRI, RED Onc), Drug Discovery (DDS) Preclinical Research (PCD) while CMC(Pharmaceutical Sciences), Regulatory, Pharmacovigilance etc.  These could include lab and computational scientists, clinicians, statisticians, and regulatory affairs professionals.
• Data architects, engineers, UI/UX engineers, product/platform/technology teams
• Vendors and external contract staff
• Relevant Industry forums / consortia in the data and information space.

WHO YOU ARE:

• MS in Biological Sciences, Informatics or Computer Science in a biomedical context
• 15+ years in data modeling (relational, object-oriented and graph based), architecture, and metadata management and controlled vocabularies for the biomedical domain.
• Deep understanding of R&D and modern quantitative methods, in particular early R&D
• Strong grasp of data governance, data quality, data catalogs, modern data science, DevOps, and cloud-native environments such as AWS, Databricks etc.
• Proven delivery of high-impact data products/solutions for Pharma R&D - vision, approach, roadmap and delivery
• Self-starter, independent yet collaborative, detail oriented, structured, process-oriented practitioner with reliable project management skills, able to influence a variety of stakeholders and manage ambiguity in a complex and changing environment.
• Strategic and conceptual thinker, effective communicator with a high learning agility, capable of translating across multiple domains and stakeholder archetypes
• Excellent written and verbal communication skills in English

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