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.
Therapy areas across Eli Lilly focus on new therapeutic approaches for the treatment of different diseases. You will work with partners across Lilly to discover and develop novel biologic, small molecule and nucleic acid-based therapeutics. Our focus is the patient: by understanding the biology and pathophysiology underlying disease states, we aim to address the root cause of disease and develop breakthrough therapies. We have one of the strongest pipelines in the industry and a track record of delivering impactful medicines that improve people’s lives. The Lilly research environment is evolving to centralize the access and analysis of human genetic, omic, and clinical data. This new initiative will work to define data, tools and process to provide the therapy area teams key evidence for target evaluation and target discovery.
We are seeking a highly specialized Applied AI Engineer Clinical Informatician to lead research at the intersection of completed clinical trial datasets and biobank-linked population data. This is fundamentally a hands-on research role (not operational trial management), where you will be an individual contributor. Your core mission is to build the systems and tools that extract, define, and contextualize patient phenotypes from locked trial databases, real-world data, and biobank cohorts, that will turn archived data that can generate translational insight that shapes the next generation of clinical research.
You will work with rich, already-collected datasets: locked trial databases, archived omics profiles, longitudinal electronic health records, and population-scale biobank cohorts. Your mandate is to build the AI and ML systems that make these datasets manageable and ready for detailed analysis. This role suits someone who thinks like a scientist, builds like an engineer, and communicates like a clinician.
Apply today!
Key Responsibilities
AI & Machine Learning for Translational Discovery
- Develop and deploy agentic AI applications that enable natural language interaction with clinical data
- Ground AI outputs in validated biological knowledge, for example implementing RAG pipelines anchored in biomedical ontologies (HPO, Gene Ontology, MeSH, DrugBank), clinical trial registries, and curated pathway databases
- Deploy unsupervised and self-supervised learning approaches like clustering, representation learning, contrastive learning to discover latent patient archetypes and molecular disease subtypes across trial and biobank data
- Deploy survival models and dynamic treatment regime estimators using combined clinical and omics features
- AI tooling to harmonize heterogeneous trial and biobank datasets to common data representations
- Evaluate and monitor model performance, safety, and reliability in production environments
- Manage vendors and contractors as well as partner relationships with relevant teams across Lilly
Post-Trial Data Research & Analysis
- Building pipelines for locked clinical trial databases (SDTM, ADaM) to conduct secondary and exploratory research beyond primary endpoints
- Deploy ML workflows to identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures from completed trial data
- Mine adverse event narratives, clinical notes, and investigator comments using NLP to surface latent safety signals not captured in structured endpoints in biobanks and clinical datasets
- Reconstruct patient-level longitudinal trajectories from trial visit data to model disease progression, drug response kinetics, and time-to-event outcomes
- Architect workflows for meta-analytic and cross-trial integrative analyses across multiple completed studies to identify generalizable biological and clinical patterns
- Build connections to large-scale biobank cohorts (UK Biobank, All of Us, etc.) as external validation and enrichment resources for trial-derived findings for clinical phenotypes
Research Rigor, Reproducibility & Governance
- Establish research data management practices ensuring full reproducibility of analyses including data versioning, containerized compute environments, and audit-ready analysis logs
- Ensure all research activities follow HIPAA, GDPR, and relevant IRB and ethics committee requirements
Basic Qualifications
- M.S. in Biomedical Informatics, Computational Biology, Bioinformatics, Statistical Genetics , Epidemiology, or a closely related quantitative field or an MD/PhD with equivalent depth in translational data science with 6+ years of research experience working with clinical trial datasets (SDTM/ADaM), biobank data, or large-scale population health data in an academic, pharmaceutical, or research institute setting
- Or Ph.D. in Biomedical Informatics, Computational Biology, Bioinformatics, Statistical Genetics, Epidemiology, or a closely related quantitative field or an MD/PhD with equivalent depth in translational data science with 3+ years of research experience working with clinical trial datasets (SDTM/ADaM), biobank data, or large-scale population health data in an academic, pharmaceutical, or research institute setting
Additional Skills & Preferences
- Demonstrated use of AI tools in production environments for clinical data analysis
- Expert proficiency in Python and/or R for statistical modelling and ML; strong command of SQL and experience with cloud-based research computing environments (ideally DNAnexus, AWS, GCP, Azure, or HPC clusters)
- Familiar with advanced generative AI methods like finetuning of LLMs. Building and training foundation models from scratch. High performance computing environments
- Deep knowledge of CDISC standards (SDTM, ADaM) and experience analyzing clinical trial databases for secondary research purposes
- Demonstrated ex