Computational Biologist - Quantitative Methods & Target Discovery
Eli Lilly and Company
4 hours ago
Remote friendly (Indianapolis, IN)
United States
Clinical Research and Development
The Opportunity
This is an individual contributor role for an experienced computational biologist to lead analyses of multimodal biological datasets and develop methods that advance target discovery in cardiometabolic disease (intersection of spatial and single-cell omics, causal inference, AI/ML, and functional genomics).
What You’ll Do
Multimodal Omics & Functional Genomics
- Design and implement single-cell and spatial omics analyses integrating imaging-based, sequencing-based, and multiplexed platforms to characterize changes in tissue architecture, cellular neighborhoods, and microenvironmental and system-level dynamics.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets, enabling discovery-ready data layers and downstream modeling.
- Lead hands-on, end-to-end functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate results with transcriptomic, proteomic, and pathway-level data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze single-cell, spatial transcriptomic, proteomic, metabolomic, and multi-omics datasets at scale.
Collaboration with Discovery, Translational & Genetics Teams
- Partner with pre-clinical bench scientists and translational biologists to frame questions, design experiments with statistical rigor, and translate computational results into target discovery decisions.
- Interpret statistical genetics outputs and integrate with functional and molecular data to build convergent evidence frameworks for target nomination.
- Develop predictive models combining genetic, functional, and multi-omics evidence to score and rank targets, using causal reasoning to distinguish association from mechanism.
- Contribute to virtual patient and disease modeling approaches supporting target validation and translational hypotheses.
Computational Methods & Platform Development
- Apply modern quantitative methods (Bayesian modeling, causal inference/causal graph modeling, mechanistic or agent-based modeling, knowledge graphs, ML/AI for target discovery and scoring) with pragmatic judgment on when each adds value.
- Evaluate and integrate novel AI approaches for multi-omics analysis (graph-based methods, generative models, representation learning, foundation models).
- Influence design and implementation of scalable, reproducible analytical workflows for high-dimensional multimodal integration; contribute to computational/data architecture for next-generation omics and ML workloads.
- Influence data architecture, pipeline design, and analytical platform standards in collaboration with data engineering and infrastructure teams.
Cross-Functional Influence
- Work with internal AI and statistics teams and Lilly Research Nucleus to leverage internally built models and co-develop new computational approaches for drug discovery.
- Champion standards in analytical rigor, reproducibility, and documentation.
- Advise fellow computational biologists through code review, collaborative analysis, and shared problem-solving.
What You Bring
Minimum requirements
- Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or a quantitative life science field, with training/research experience combining analytical method development (Bayesian approaches, AI/ML, etc.) with applied work in multi-omics, spatial omics, or functional genomics.
Preferred
- 2+ years of post-doctoral or biopharma/biotech industry experience.
- Experience with spatial omics platforms (spatial transcriptomics, multiplexed imaging, spatial proteomics), single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
- Proficiency in Python and/or R with strong software practices (version control, documentation, reproducible workflows) and familiarity with scientific computing libraries.
- Familiarity with workflow orchestration (e.g., Nextflow), cloud-native analytical environments, and research data architecture.
- Demonstrated ability to analyze, integrate, and interpret large-scale multimodal datasets and design scalable analytical pipelines.
- Demonstrated experience in at least two of: Bayesian methods (e.g., PyMC, Stan), causal modeling, knowledge graphs, ML/AI for biological target discovery, causal inference, or functional genomics analysis at scale.
- Ability to interpret statistical genetics outputs and integrate with molecular/functional data (not required to run GWAS).
- Track record of collaboration with experimental scientists, statisticians, AI teams, and/or other computational scientists across organizational boundaries.
- Experience building predictive models or integrative evidence frameworks combining genetic and functional data for target scoring.
- Experience developing or contributing to novel ML/AI models or statistical/algorithmic approaches in a biological context.
- Track record of leading through scientific influence (owning complex research programs, setting technical direction, shaping priorities without direct management authority).
- Strong publication record in peer-reviewed journals or ML/AI venues.
Compensation & Benefits (as stated)
- Anticipated wage: $166,500 - $266,200.
- Company bonus (for eligible full-time equivalent employees, depending on company and individual performance).
- Comprehensive benefits for eligible employees, including 401(k), pension, vacation, medical/dental/vision/prescription coverage, flexible benefits, life insurance, time off/leave of absence benefits, and well-being benefits.
Application instructions (as stated)
- If you require an accommodation to submit a resume, complete the accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation
This is an individual contributor role for an experienced computational biologist to lead analyses of multimodal biological datasets and develop methods that advance target discovery in cardiometabolic disease (intersection of spatial and single-cell omics, causal inference, AI/ML, and functional genomics).
What You’ll Do
Multimodal Omics & Functional Genomics
- Design and implement single-cell and spatial omics analyses integrating imaging-based, sequencing-based, and multiplexed platforms to characterize changes in tissue architecture, cellular neighborhoods, and microenvironmental and system-level dynamics.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets, enabling discovery-ready data layers and downstream modeling.
- Lead hands-on, end-to-end functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate results with transcriptomic, proteomic, and pathway-level data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze single-cell, spatial transcriptomic, proteomic, metabolomic, and multi-omics datasets at scale.
Collaboration with Discovery, Translational & Genetics Teams
- Partner with pre-clinical bench scientists and translational biologists to frame questions, design experiments with statistical rigor, and translate computational results into target discovery decisions.
- Interpret statistical genetics outputs and integrate with functional and molecular data to build convergent evidence frameworks for target nomination.
- Develop predictive models combining genetic, functional, and multi-omics evidence to score and rank targets, using causal reasoning to distinguish association from mechanism.
- Contribute to virtual patient and disease modeling approaches supporting target validation and translational hypotheses.
Computational Methods & Platform Development
- Apply modern quantitative methods (Bayesian modeling, causal inference/causal graph modeling, mechanistic or agent-based modeling, knowledge graphs, ML/AI for target discovery and scoring) with pragmatic judgment on when each adds value.
- Evaluate and integrate novel AI approaches for multi-omics analysis (graph-based methods, generative models, representation learning, foundation models).
- Influence design and implementation of scalable, reproducible analytical workflows for high-dimensional multimodal integration; contribute to computational/data architecture for next-generation omics and ML workloads.
- Influence data architecture, pipeline design, and analytical platform standards in collaboration with data engineering and infrastructure teams.
Cross-Functional Influence
- Work with internal AI and statistics teams and Lilly Research Nucleus to leverage internally built models and co-develop new computational approaches for drug discovery.
- Champion standards in analytical rigor, reproducibility, and documentation.
- Advise fellow computational biologists through code review, collaborative analysis, and shared problem-solving.
What You Bring
Minimum requirements
- Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or a quantitative life science field, with training/research experience combining analytical method development (Bayesian approaches, AI/ML, etc.) with applied work in multi-omics, spatial omics, or functional genomics.
Preferred
- 2+ years of post-doctoral or biopharma/biotech industry experience.
- Experience with spatial omics platforms (spatial transcriptomics, multiplexed imaging, spatial proteomics), single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
- Proficiency in Python and/or R with strong software practices (version control, documentation, reproducible workflows) and familiarity with scientific computing libraries.
- Familiarity with workflow orchestration (e.g., Nextflow), cloud-native analytical environments, and research data architecture.
- Demonstrated ability to analyze, integrate, and interpret large-scale multimodal datasets and design scalable analytical pipelines.
- Demonstrated experience in at least two of: Bayesian methods (e.g., PyMC, Stan), causal modeling, knowledge graphs, ML/AI for biological target discovery, causal inference, or functional genomics analysis at scale.
- Ability to interpret statistical genetics outputs and integrate with molecular/functional data (not required to run GWAS).
- Track record of collaboration with experimental scientists, statisticians, AI teams, and/or other computational scientists across organizational boundaries.
- Experience building predictive models or integrative evidence frameworks combining genetic and functional data for target scoring.
- Experience developing or contributing to novel ML/AI models or statistical/algorithmic approaches in a biological context.
- Track record of leading through scientific influence (owning complex research programs, setting technical direction, shaping priorities without direct management authority).
- Strong publication record in peer-reviewed journals or ML/AI venues.
Compensation & Benefits (as stated)
- Anticipated wage: $166,500 - $266,200.
- Company bonus (for eligible full-time equivalent employees, depending on company and individual performance).
- Comprehensive benefits for eligible employees, including 401(k), pension, vacation, medical/dental/vision/prescription coverage, flexible benefits, life insurance, time off/leave of absence benefits, and well-being benefits.
Application instructions (as stated)
- If you require an accommodation to submit a resume, complete the accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation