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 35,000 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.
Position Overview
Lilly is a global healthcare leader headquartered in Indianapolis, Indiana, USA with many global locations including our LCCI site located in Bangalore, India. Our 34,000 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 are looking for people who are determined to make life better for people around the world.
The Synthetic Molecule Design and Development (SMDD) Organization within the Lilly Research Labs is an integrated synthetic molecule drug development organization which spans drug discovery and development phases from early discovery to submission and post-approval. The automation and data sciences team within SMDD is a cross-functional group of chemists, engineers, and computational chemists with a broad range of computational modeling and data science capabilities complemented by engineering and synthetic organic chemistry competencies. This group drives innovation in the drug substance development process through the efficient combination of organic synthesis expertise coupled with computational modeling, automation, data sciences, and data rich experimentation.
The data scientist will transform data and information captured within legacy systems into value-added insights and actionable business decisions through the use of predictive analytics. Candidates must demonstrate a history of strong technical depth, a broad base of modeling and information technology skills, scientific leadership and a willingness to promote a collaborative, team-based approach to problem solving. Specifically, the candidate will have experience in the following areas:
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