Overview In this role you will provide statistical support to scientists in Lilly's Product Development (nonclinical) to enable invention of novel medicines across molecules and modalities. You will design experiments, analyze data, and develop methods to advance drug development and manufacturing, including analytical method transfer and stability studies. You will contribute to CMC regulatory submissions and collaborate across biology, chemistry, formulation, and engineering teams. You will communicate results clearly and mentor others, shaping rigorous statistical practices in a patient-centric mission.
Compensation / Benefits- bonus program
- 401(k) plan
- medical/dental/vision insurance
- pension
- paid time off
- flexible spending accounts
Responsibilities- Statistical support for experimental design and analysis across chemists, biologists, formulators, and analytical teams
- Design and analyze stability studies for clinical and commercial specifications
- Develop novel statistical methods to advance drug development and manufacturing
- Contribute to CMC regulatory submission sections for global approvals
- Collaborate on applied research in modeling, process control, and multivariate analysis
- Communicate study results with clarity to internal teams and external partners
- Present results at scientific meetings and in manuscripts
- Provide training on statistical methods to colleagues
Key requirements- Ph.D. or M.S. with extensive experience in statistics/biostatistics or related field
- Experience in pharmaceutical development or manufacturing
- Proficiency in R, Python, JMP and/or SAS
- Expertise in DOE, Bayesian statistics, machine learning, visualization
- Strong English communication and collaboration skills
- Ability to acquire and compile data from multiple sources
- Strong problem solving, strategic thinking, and leadership skills
- Interest in chemistry, biology, engineering, and pharmaceutical science
- Interpersonal teamwork
- Effective communication
- Leadership
- DOE
- Bayesian statistics
- Machine learning