Within the Biologics CMC Development organization, the incumbent will support the development of formulations, manufacturing processes, and testing of protein biologic drug products. We support projects from early development through commercialization, with a specific focus on select agents for therapeutic and aesthetic use. This position requires laboratory work in areas requiring select agents (i.e., Biosafety level 2 laboratories).
The ideal candidate will have hands-on experience with analytical techniques used for protein characterization and stability testing, as well as a demonstrated ability to drive innovative science in a team setting. Experience with laboratory automation, automated sample preparation, high-throughput screening, instrument integration, or automated data workflows is highly desirable.
Responsibilities
- Plans and executes product formulation & process development activities in support of the development of new products intended for new biologic entities and life-cycle management of marketed products with a focus on highly potent biologic drug products.
- Generate high-quality data based on sound scientific principles and prepare formulation data summaries, technical reports, scientific presentations, and regulatory documents.
- Participate in drug product development teams and contribute to the planning and execution of product development studies including formulation and drug product process development, based on project goals and timelines.
- Actively interact with CMC members and other AbbVie stakeholders to enable the successful development of AbbVie drug products.
- Drive innovation by investigating and developing novel experimental approaches and/or technologies in the field of drug product development and raising visibility within the scientific community through external presentations and publications.
- Develop and implement laboratory automation systems and workflows to support high-throughput formulation screening, analytical testing, data generation, and process optimization while improving efficiency, reproducibility, and data quality.