AbbVie Data Science is recognized as a best-in-class team within its cross-industry peer group, dedicated to integrating people, processes, and technology to derive business value from clinical trials data. The operational model of this team is characterized by a strong emphasis on execution and innovation, which is crucial for the successful delivery of program- and study-level accountabilities assigned to Data and Statistical Sciences. The Program Lead I - Data Science plays a pivotal role in aligning the Data Science Study (DSS) teams with the overarching program and study strategies. This position is responsible for leading the DSS Study Team for assigned studies and serves as the primary operational lead from DSS, ensuring that all operational objectives are met efficiently. In this role, the Program Lead will coordinate with various DSS study teams, engaging and connecting global functional and cross-functional teams to achieve program and study objectives. The individual will utilize operational analytics and project management tools to optimize the execution of programs and studies, manage both internal and external resources, track study progress, and prepare comprehensive study status reports. A key aspect of this position involves anticipating and identifying potential issues that could impact timelines or quality, and developing viable options and solutions to address these challenges. The Program Lead is also responsible for ensuring compliance with federal regulations, local regulations, Good Clinical Practices, ICH Guidelines, and AbbVie Standard Operating Procedures (SOPs). Staying updated on evolving regulations and guidelines related to clinical development is essential. Additionally, the role includes oversight of vendors, providing feedback on study operations, and mentoring Data Science Associates, which may involve indirect supervision of contract resources. Participation in innovation and process improvement initiatives within DSS and across functions is also a critical responsibility, along with contributing to the documentation of study execution "lessons learned" across various functions.