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Applied AI Engineer, Clinical Informatics — Eli Lilly, Boston Onsite
Develop applied AI and machine learning capabilities for Eli Lilly’s clinical trials, biobanks, and translational medicine programs. Use clinical, molecular, and population data to characterize patient phenotypes and generate evidence for future therapeutics.
संयुक्त राज्य
मैसाचुसेट्स$1,66,500 – $2,66,200 / वर्ष
ऑन-साइटपूर्णकालिकमध्य स्तरवरिष्ठआवश्यक भाषा: अंग्रेज़ीAI इंजीनियरमशीन लर्निंग इंजीनियरविवरण
- Create and productionize agentic AI tools that let researchers query clinical data in natural language
- Use biomedical ontologies, trial registries, and curated pathway resources to ground retrieval-augmented generation outputs in verified biological knowledge
- Apply unsupervised and self-supervised methods to identify patient archetypes and molecular disease subtypes across trial and biobank datasets
- Build survival analyses and dynamic treatment-regime estimators from integrated clinical and omics variables
- Standardize diverse clinical-trial and biobank sources into shared data representations
- Assess and continuously monitor model quality, safety, and operational reliability after deployment
- Coordinate vendors, contractors, and external partners supporting Lilly research programs
- Develop SDTM- and ADaM-based pipelines for locked trial databases used in secondary and exploratory analyses
- Analyze completed trials for subgroup effects, treatment heterogeneity, and responder or non-responder patterns
- Apply NLP to adverse-event narratives, clinical notes, and investigator commentary to uncover potential safety signals
- Reconstruct longitudinal patient histories to study disease progression, treatment-response kinetics, and time-to-event endpoints
- Design cross-study meta-analysis and integrative workflows spanning completed clinical trials
- Use large biobank cohorts, including UK Biobank and All of Us, for external validation and data enrichment
- Implement reproducible research operations with data version control, containerized computing, and audit-ready analysis records
- Conduct research in accordance with HIPAA, GDPR, and applicable IRB and ethics committee standards
आवश्यकताएँ
- Master’s degree in biomedical informatics, computational biology, bioinformatics, statistical genetics, epidemiology, computer science, or a related quantitative discipline, or an MD/PhD with equivalent translational data-science depth, plus at least six years of research experience using clinical-trial datasets (SDTM/ADaM), biobank data, or large population-health datasets
- Doctoral degree in biomedical informatics, computational biology, bioinformatics, statistical genetics, epidemiology, computer science, or a related quantitative discipline, or an MD/PhD with equivalent translational data-science depth, plus at least three years of research experience using clinical-trial datasets (SDTM/ADaM), biobank data, or large population-health datasets
- Proven experience deploying AI tools in production for clinical-data analysis
- Advanced Python and/or R skills for machine learning and statistical modeling
- Strong SQL proficiency
- Experience using cloud research-computing platforms, preferably DNAnexus, AWS, Google Cloud, Azure, or high-performance computing clusters
- Working knowledge of advanced generative-AI techniques, including LLM fine-tuning and training foundation models from the ground up
- Experience working in high-performance computing environments
- Comprehensive knowledge of CDISC standards, including SDTM and ADaM
- Demonstrated application of survival analysis, causal inference, NLP, and deep learning to clinical or genomic research
- Strong understanding of OMOP CDM, HL7 FHIR Genomics, and leading biomedical ontologies
- Hands-on research with major public and restricted-access biobanks, including UK Biobank and All of Us
- Experience applying federated learning, differential privacy, or secure-computation methods to multisite biomedical research
- Peer-reviewed publication record in clinical AI, translational informatics, genomics, or a related discipline
- Familiarity with the target-trial framework and its use with biobank data
- Knowledge of pharmacogenomics, drug-response modeling, or clinical-trial PK/PD analysis
- Experience building biomedical knowledge graphs, applying graph machine learning, or using ontology-based reasoning
- Practical experience analyzing multi-omic datasets
- Ability to comply with HIPAA, GDPR, and applicable IRB and ethics committee requirements
लाभ
- Eligibility for a company bonus influenced partly by organizational and individual results
- Employer-sponsored 401(k) plan
- Pension plan
- Vacation time
- Medical coverage
- Dental coverage
- Vision coverage
- Prescription drug coverage
- Flexible spending options for healthcare and/or dependent care
- Life insurance and death benefits
- Time off and leave programs
- Well-being support such as an employee assistance program, fitness offerings, and employee clubs and activities
- Employee resource groups
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