At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world. When it comes to research and development, our goal is to discover and deliver innovative medicines that make life better for people around the world. It’s challenging, expensive and often filled with failure. But even when we fail, we advance medical science and understanding by learning more about diseases, biology and chemistry – ultimately bringing new solutions one step closer to reality. Over the course of our history, we have shed light on some of the toughest health care problems known to man – diabetes, heart disease, infectious diseases, neuroscience disorders, cancer and more. We could not pursue this without our research and development team. Postdoctoral scientists help us continue this pursuit. During your experience you will get: Top industry research experience Mentoring by some of Lilly’s top scientists Laboratory and classroom training and education to further your development (as needed) Collaboration and networking across dozens of postdoctoral scientists and other researchers We are seeking an ambitious post-doctoral scientist with strong research and communication skills to contribute to the advancement of machine learning (ML)–based approaches for identifying risk factors associated with key safety topics. Purpose of the Job In this role, you will utilize innovative ML and rare event modeling techniques to investigate risk factors and protective factors associated with serious or severe drug adverse events that have not been extensively studied to date. The initial focus will be on neuroscience, with the methodology subsequently extended to other critical events and therapeutic areas. This work will enhance our understanding of the drivers behind these safety concerns and strengthen our ability to evaluate the benefit-risk profile of investigational and marketed therapies. This work will support the activities of the Global Patient Safety (GPS) Medical organization which characterizes and communicates the safety profile of Lilly medicines, assesses benefit-risk, and plans and measures risk minimization measures across Lilly’s portfolio. GPS Medical partners with the business units, Global Scientific Communication, Global Regulatory Affairs, Statistics, and other functions to develop clinical plans, influence study protocol design, conduct medical surveillance, and more. Analysis of safety data is key to achieving these goals; therefore, the Advanced Intelligence group is another key partner liaising with GPS Medical. Advanced Intelligence leverages advanced statistical capabilities (including ML and other types of artificial intelligence) to enhance safety data analysis. The main expected outcome of this project is the generation of scientific data revealing new clinical insights into risk factors driving adverse events of special interest for key Lilly products. Further, the methodology will be adapted to other compounds both in development and in the market. These findings offer strong potential for publication in high-impact peer-reviewed journals, advancing scientific understanding of disease processes and benefiting stakeholders across industry, government, and academia. The candidate will develop key skills in pharmacovigilance and safety science (particularly with respect to novel methodologies), have extensive professional networking opportunities via collaborations with colleagues in Medical, Statistics, Toxicology, and Advanced Intelligence, gain experience in the pharmaceutical industry at multiple lifecycle stages, and leverage opportunities to share findings with the external scientific community.
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Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees