Post Doctoral Fellowship: Global Patient Safety at Lilly Indianapolis

Purpose of the Job

In this role, you will utilize innovative machine learning 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 therapeutic areas. This work will enhance our understanding of the drivers behind these safety concerns and strengthen the benefit‑risk profile of investigational and marketed therapies.

• Develop and validate machine‑learning models to predict safety outcomes, incorporating risk factors such as patient histories, concomitant medications, imaging, and other clinical parameters. Employ ensemble methods, resampling strategies, cost‑sensitive learning, and anomaly detection to address rare‑event prediction and improve model robustness.
• Present findings at conferences and publish in high‑impact peer‑reviewed journals.
• Understand current medical surveillance and signal‑management processes, leverage insights from adverse events of special interest, and recommend improvements to routine medical surveillance. Explore integration and standardization of new methodologies into existing safety‑data analysis platforms to enhance consistency and accuracy across products.
• Communicate effectively with internal business partners, including early‑phase medicine and business unit teams and regulatory scientists, and collaborate on issue management responses to regulatory authorities. Share findings and collaborate on methodologies with Advanced Intelligence and external partners.
• Act as a subject‑matter expert in safety‑quality system documents and training tools, provide medical support to Global Patient Safety safety teams, demonstrate knowledge of global regulatory requirements, support audit readiness, and champion creative solutions to address safety‑related needs.

• An advanced analytical, statistical, bioinformatics, or medical‑related graduate degree (Ph.D., PharmD).

• Competency in independent analysis of data and interpretation of results from clinical studies.
• Excellent computer skills and interdisciplinary experience in statistical modeling and computer sciences, including machine learning.
• Proficiency in programming languages including Python and R with experience using key ML libraries (e.g., scikit‑learn, XGBoost, Tensor Flow).
• Self‑directed, highly motivated, with a desire to learn new techniques while continually pursuing research and publication goals.
• Excellent oral and written communication, presentation skills, and a strong publication record.
• Proficiency in data analysis and reporting.
• Experience in drug safety is not required.

• This position is located in Indianapolis, Indiana (office‑based); remote is not considered.
• This is a fixed‑term, two‑year position with a potential extension to three years. Opportunities to apply for full‑time positions will be available after completion.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is $58,000 – $123,200. Full‑time equivalent employees are also eligible for a company bonus (depending, in part, on company and individual performance). Lilly offers a comprehensive benefit program, including a company‑sponsored 401(k), pension, vacation, medical, dental, vision, prescription drug benefits, flexible spending accounts, life and death insurance, time‑off and leave of absence benefits, employee assistance program, fitness benefits, and employee clubs and activities.

Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion.