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R&D Data Management Reference Data Tech 

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

Bayer’s reference data / standards are housed in OneEDG – a system based on Top Quadrant’s TopBraid EDG and in MARLAY, a Databricks solution. The R&D Reference Data Technology Steward role will be responsible for the establishment of reference data standards and update in OneEDG and MARLAY. While this role deals with the technical aspects of ontology set up, it is in essence a business facing role and not a technology focused role.

STAKEHOLDERS

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), Clinical Development, Regulatory, Pharmacovigilance etc.  These could include lab and computational scientists, clinicians, statisticians, and business operations professionals.
• Data architects, engineers, UI/UX engineers, product/platform/technology teams
• Vendors and external contract staff

ROLES AND RESPONSIBILITIES

• Act as a bridge between the R&D Data Strategy & Governance team, scientific data users (scientists/ data scientists), and IT implementation teams to support the establishment of reference and master data assets in OneEDG, in conjunction
• Collaborate with stakeholders across R&D and business stewards to assess reference and master data related needs, usage expectations of reference data sets and their impact on establishment of reference datasets
• Ensure that the technical foundation for reference data (systems, architecture, tools) is aligned with business objectives and technical needs
• Design reference data solutions aligned with business goals and technical needs such as semantic modeling and AI solutions
• Implement business-defined mappings (e.g., industry classification, management hierarchies) to support consistent reporting
• Create new data structures or update/change existing data structures in OneEDG and/or MALAY or lifecycle management of reference data
• Perform limited technical adaptations in OneEDG, e.g., new columns, via the user interface of OneEDG. There is no expectation of coding.
• Oversee reference data workflows from ingestion of standards to access and consumption in business workflows, ensuring timely and effective delivery of reference data to consuming workflow and maintain traceability of use of reference data across workflows
• Create and maintain documentation and act as an expert guide on data definitions, data flows, data models etc. for applicable reference data
• Provide training and ongoing support to R&D teams on tools and technologies used in reference and master data management.
• Promote awareness and adoption of data standards, governance principles, and best practices across R&D functions.

WHO YOU ARE

1. MS/PhD in Biological Sciences, Informatics or Computer Science in a biomedical context
2. 8+ years in data modeling (relational, object-oriented and graph based), architecture, and metadata management and controlled vocabularies for the biomedical domain.
3. Deep understanding of ontologies, knowledge graphs, semantic data representation, common industry reference data standards such as NCIT, NCBI, SPOR, CDISC, IDMP, and USDM etc. and technologies such as AWS, Databricks etc.
4. Strong grasp of data governance including access rights and user management, data quality, data catalogs, modern data science, DevOps
5. Proven experience managing life sciences or medical data, including data modeling and high-level ETL pipeline design and working with TopBraid EDG or related ontology management systems; basic programming skills SQL, Python, and SPARQL, and standards like OWL, SKOS, and SHACL.
6. 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.
7. Effective communicator with a high learning agility, capable of translating across multiple domains and stakeholder archetypes, between business and technical disciplines
8. Excellent written and verbal communication skills in English

Ever feel burnt out by bureaucracy? Us too. That’s why we’re changing the way we work— for higher productivity, faster innovation, and better results. We call it Dynamic Shared Ownership (DSO). Learn more about what DSO will mean for you in your new role here  https://www.bayer.com/en/strategy/strategy
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