AbbVie - Durham, NC
posted about 2 months ago
The Senior Associate, Clinical Analytics role at AbbVie is a pivotal position within the Data Science and Analytics team, which is composed of Data Scientists and Data Engineers. This team is dedicated to leveraging AbbVie's extensive data assets to enhance decision-making through innovative technology and data insights. The primary focus of this role is to apply advanced analytics techniques to assess the feasibility of clinical trials at various levels, including program, study, country, and site. The Senior Analyst will drive analytics innovation and experimentation, utilizing machine learning and visual analytics to generate data-driven insights that address complex business challenges in research and development (R&D). In this role, the Senior Associate will collaborate with cross-functional teams to design analytical work products and serve as an analytics consultant. They will proactively identify potential issues that could impact project timelines or quality, developing viable solutions to mitigate these risks. The responsibilities include conducting comprehensive analyses related to clinical trial feasibility, which encompasses study design, benchmarking, country and site selection insights, and competitive landscape assessments. The Senior Associate will also utilize analytics solutions to create machine learning-informed site lists and establish design principles to ensure consistency and optimize user experience across projects. Furthermore, the role involves ensuring compliance with federal regulations, local guidelines, Good Clinical Practices, and AbbVie Standard Operating Procedures. The Senior Associate will stay updated on evolving regulations and policies related to clinical development. They will also identify business needs, support the creation of standard KPIs, and mentor junior team members. By working closely with cross-functional teams, the Senior Associate will strategize on how analytics can enhance the evaluation of clinical trial progress and facilitate discussions around emerging risks, applying machine learning techniques to validate assumptions and predict future behaviors.