Job Description

About Company:
Our client is a global life sciences company committed to improving access to healthcare and supporting the people we serve throughout the continuum of care. From prevention to treatment, Our client transforms scientific innovation into healthcare solutions, in human vaccines, rare diseases, multiple sclerosis, oncology, immunology, infectious diseases, diabetes and cardiovascular solutions and consumer healthcare. More than 110,000 people in over 100 countries at Our client is dedicated to making a difference on patients' daily life, wherever they live and enabling them to enjoy a healthier life.
An Ideal Candidate:
The R&D Data Modeler develops conceptual and logical data models for initiatives, programs, and cross-R&D capabilities. This role is critical for creation of data models and ontologies, and upkeep of models even beyond the conclusion of a project. Data modelers will apply and assist in the definition and governance of data modelling and design standards, tools, best practices, and related development of any R&D data capability.
Key Competencies:
1.Understand the data-related needs for various cross-R&D capabilities (E.g., data catalog, master data management etc) and associated initiatives
Design conceptual and logical data models and data dictionaries/ontologies to cater to R&D business needs and functional requirements; lead validation of physical data models
2.Interact with business, R&D Digital, and other data collaborators to translate needs into data solutions
3.Understand data governance policies, standards and procedures for R&D data
4.Maintain modelling and naming standards, and data harmonization, metadata management, and source-to-target data mapping documentation
5.Evaluate and influence projects while serving as "voice-of-business"; map systems/interfaces for data management, set standards for future state, and close gap from current-to-future state
6.Serve as technical and data consultant for R&D initiatives with major platforms/technology implementations
7.Maintain documentation and act as an expert on data definitions, data standards, data flows, legacy data structures / hierarchies, common data models, data harmonization etc. for R&D functions
8.Educate and guide R&D teams on standards and information management principles, methodologies, best practices, etc.