Director, R&D Data Management Expert - Data Science Ecosystem Data Product Lead

Bayer•Cambridge, MA

21h

About The Position

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. Director, R&D Data Management Expert - Data Science Ecosystem Data Product Lead Bayer is transforming pharma R&D through data centricity, leveraging automation and AI/ML to revolutionize research processes. The Data Science Ecosystem (DSE) serves as a unified platform for pharma R&D data, analytics, and AI/ML products, with access to Bayer's R&D data assets and capabilities such as multi-modal data integration that make data discoverable, accessible, providing near real-time decision-making in R&D. This role sits in Pharmaceutical R&D Data Science and AI / Data Strategy and Governance and, works directly with the DSE team to design and deliver DSE. You will drive the development of DSE data capabilities, pulling together data capabilities managed by respective product owners, prioritizing requirements, and shaping the roadmap to deliver on DSE use cases. This is a business role and not a technological role although a sound understanding of modern technology will be valuable and is desired. You'll be solving hard technical-strategic problems: How do we build data capabilities in a fit for purpose mode while allowing for scale? How do we build systems that balance data democratization with security and regulatory requirements? How do we manage ambiguity when capabilities originate from multiple sources and involve diverse stakeholders?

Requirements

Advanced degree (MS/PhD) in Life Sciences, Computer Science, Bioinformatics, or related field
Experience in scientific data management, data architecture, or research informatics, preferably in pharma/biotech R&D
Deep understanding of R&D processes and data challenges
Strong knowledge of data capabilities e.g. catalog, data models, metadata and their applications as well as modern technology stacks e.g. AWS, Databricks etc.
Experience as a digital product owner with the ability to envision the target state while architecting and defining pragmatic incremental steps, ability to manage and track to implementation, requirements for multiple data capabilities based on use case prioritization, Track record of translating ambiguous strategic needs into concrete technical solutions
Flair for communication with the ability to engage credibly with both scientists and engineers, articulate complex technical-governance tradeoffs, building consensus across diverse groups with competing priorities, yet concluding on decisions
Ability to work in an agile fashion, managing ambiguity across dimensions

Nice To Haves

10+ years in scientific data management, data architecture, or research informatics, preferably in pharma/biotech R&D
Agile product development, GxP, Omics, Data Governance, Data Security, Program Management, Use Case Analyses
Understanding of R&D including omics data types (NGS, proteomics, imaging)
Familiarity with FAIR principles and practical implementation challenges
Experience with data governance in regulated environments (GxP, GDPR)
Knowledge of German

Responsibilities

Product ownership of DSE data capabilities: Consolidate and prioritize requirements across use cases and data capability product owners; define and deliver roadmap for DSE use cases
Co-create the vision for data capabilities with respective product teams, ensuring alignment with organizational strategy
Promote and operationalize FAIR (Findable, Accessible, Interoperable, Reusable) data principles that balance rigor with pragmatism
Facilitate cross-functional collaboration to ensure efficient and compliant data management
Support and implement the technical and governance frameworks for data access, use and reuse
Continuously monitor industry trends and best practices to enhance data capabilities.