AbbVie Data Science is the best-in-class team within its cross-industry peer group and is responsible for bringing people, process, and technology together to generate business value from clinical trials data. Our operational model is exemplified through execution and innovation. This role is key to ensuring successful delivery against the program- and study-level accountabilities assigned to Data and Statistical Sciences.
This role is hybrid. Onsite on Tuesday, Wednesday and Thursday. Remote on Mondays and Fridays.
Responsibilities
- Aligns DSS study teams with program- and study-level strategies. For assigned studies, leads the DSS Study Team and represents DS as a member of the cross-functional study team
- For assigned studies, acts as single point of contact and accountable operational lead from DSS. Coordinates associated DSS study teams to meet operational objectives. Engages and connects global functional and cross-functional teams
- Interacts with and influences cross-functional team members to achieve program and study objectives
- Utilizes operational analytics and project management tools to optimize execution of programs and studies, to manage internal and external resources, to track study progress, and to prepare study status reports. Anticipates and identifies issues that could affect timelines or quality and develops options and solutions
- Ensures adherence to federal regulations and applicable local regulations, Good Clinical Practices (GCPs), ICH Guidelines, AbbVie Standard Operating Procedures (SOPs), and to functional quality standards. Stays abreast of new and/or evolving local regulations, guidelines and policies related to clinical development
- Participates in oversight of vendors and provides feedback related to study operations, issues, and trends in performance
- Responsible for coaching and mentoring Data Science Associates. May include indirect supervision of work for contract resources
- Participates in DSS and cross-functional innovation and process improvement initiatives
- Contributes to study execution “lessons learned” across functions
