Cdisc Sdtm Adam Jobs in Indiana (NOW HIRING)

Job Summary:
Eli Lilly and Company is a global healthcare leader headquartered in Indianapolis, Indiana, focused on discovering and bringing life-changing medicines to those in need. They are seeking a highly specialized Applied AI Engineer Clinical Informatician to lead research at the intersection of clinical trial datasets and biobank-linked population data, building systems and tools for extracting and contextualizing patient phenotypes from various datasets.
Responsibilities:
• 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
• 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
• 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
Qualifications:
Required:
• 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
Preferred:
• 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 experience applying ML methods including survival analysis, causal inference, NLP, and deep learning to clinical or genomic research questions
• Thorough understanding of OMOP CDM, HL7 FHIR Genomics, and major biomedical ontologies
• Direct research experience with major public and restricted-access biobank resources (UK Biobank, All of Us, etc.)
• Experience with federated learning, differential privacy, or secure computation frameworks applied to multi-site biomedical research
• Track record of peer-reviewed publications in clinical AI, translational informatics, genomics, or a related field
• Familiarity with the target trial framework and its application in biobanks
• Knowledge of pharmacogenomics, drug response modeling, or PK/PD data analysis from clinical trials
• Experience with knowledge graph construction, graph ML, or ontology-driven reasoning for biomedical discovery
• Hands-on experience with multi-omic data analysis
Company:
We're a medicine company turning science into healing to make life better for people around the world. Founded in 1876, the company is headquartered in Indianapolis, USA, with a team of 10001+ employees. The company is currently Late Stage.