Computational Biologist - Quantitative Methods & Target Discovery
Eli Lilly and Company
4 hours ago
Remote friendly (Boston, MA)
United States
Clinical Research and Development
The Opportunity / Role Summary
- Individual contributor computational biologist leading analyses of multimodal biological datasets and developing methods to advance target discovery in cardiometabolic disease (intersection of spatial and single-cell omics, causal inference, AI/ML, and functional genomics).
Responsibilities
- Independently design and implement end-to-end analyses of spatial and single-cell transcriptomic, proteomic, and metabolomic datasets, plus functional genomics workstreams.
- Integrate results across modalities and with genetic evidence to build convergent frameworks for target prioritization.
- Develop predictive models to score targets, distinguish association from mechanism, and provide confidence measures to inform portfolio decisions.
- Advance quantitative toolkit: introduce ML/AI approaches, knowledge graphs, Bayesian methods, and causal modeling.
- Influence data architecture and analytical standards to support reproducible, scalable science.
- Collaborate with internal AI, data engineering, translational biology, statistical genetics, and statisticians to leverage and co-develop models for drug discovery.
What You'll Do
- Multimodal Omics & Functional Genomics
- Design and implement single-cell and spatial omics analyses integrating imaging-, sequencing-, and multiplexed-platform data.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets.
- Perform end-to-end functional genomics analyses (e.g., CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic, proteomic, and pathway-level data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze multi-omics datasets at scale.
- Collaboration with Discovery, Translational & Genetics teams
- Partner with bench and translational scientists to frame questions, design experiments with statistical rigor, and translate computational results into target discovery decisions.
- Interpret statistical genetics outputs and integrate with functional/molecular data for convergent target evidence.
- Develop predictive models combining genetic, functional, and multi-omics evidence to score/rank targets using causal reasoning.
- Contribute to virtual patient/disease modeling to support target validation and translational hypotheses.
- Computational Methods & Platform Development
- Apply modern quantitative methods (Bayesian modeling, causal inference/graphs, mechanistic/agent-based modeling, knowledge graphs, ML/AI for target discovery/scoring) with pragmatic judgment.
- Evaluate and integrate novel AI approaches (graph-based methods, generative models, representation learning, foundation models).
- Influence scalable, reproducible analytical workflows for high-dimensional multimodal integration.
- Influence data architecture, pipeline design, and analytical platform standards with data engineering/infrastructure teams.
- Cross-Functional Influence
- Work with internal AI/statistics teams (and Lilly Research Nucleus) to leverage models and co-develop new approaches for drug discovery.
- Champion standards in analytical rigor, reproducibility, and documentation; advise via code review and collaborative problem-solving.
Qualifications
- Minimum requirements
- Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or a quantitative life science field; training/research combining analytical method development (Bayesian/AI-ML, etc.) with applied work in multi-omics, spatial omics, or functional genomics.
- Preferred
- 2+ years post-doctoral or biopharma/biotech industry experience.
- Experience with spatial omics (spatial transcriptomics, multiplexed imaging, spatial proteomics), single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
- Proficiency in Python and/or R with solid 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/interpret large-scale multimodal datasets and design scalable analytical pipelines.
- Demonstrated experience in at least two: Bayesian methods (e.g., PyMC, Stan), causal modeling, knowledge graphs, ML/AI for biological target discovery, causal inference, or functional genomics at scale.
- Ability to critically interpret statistical genetics outputs (understand what outputs mean and how to use them; not required to run GWAS).
- History of cross-functional collaboration with experimental scientists, statisticians, AI teams, and/or other computational scientists.
- Experience building predictive models or integrative evidence frameworks for target scoring.
- Experience developing/contributing to novel ML/AI models or statistical/algorithmic approaches in a biological context.
- Track record of leading through scientific influence (owning programs, setting technical direction, shaping priorities without direct management authority).
- Strong publication record in peer-reviewed journals or ML/AI venues.
Benefits (explicitly listed)
- Anticipated wage: $166,500 - $266,200.
- Company bonus eligibility (for full-time equivalent employees; depends on company and individual performance).
- Comprehensive benefit program for eligible employees, including: 401(k), pension, vacation, medical/dental/vision/prescription; flexible benefits (e.g., healthcare and/or dependent day care FSA); life insurance/death benefits; certain time off/leave of absence benefits; well-being benefits (e.g., employee assistance program, fitness benefits, employee clubs/activities).
Application instructions (explicitly stated)
- If you require accommodation to submit a resume, complete the accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation.
- Individual contributor computational biologist leading analyses of multimodal biological datasets and developing methods to advance target discovery in cardiometabolic disease (intersection of spatial and single-cell omics, causal inference, AI/ML, and functional genomics).
Responsibilities
- Independently design and implement end-to-end analyses of spatial and single-cell transcriptomic, proteomic, and metabolomic datasets, plus functional genomics workstreams.
- Integrate results across modalities and with genetic evidence to build convergent frameworks for target prioritization.
- Develop predictive models to score targets, distinguish association from mechanism, and provide confidence measures to inform portfolio decisions.
- Advance quantitative toolkit: introduce ML/AI approaches, knowledge graphs, Bayesian methods, and causal modeling.
- Influence data architecture and analytical standards to support reproducible, scalable science.
- Collaborate with internal AI, data engineering, translational biology, statistical genetics, and statisticians to leverage and co-develop models for drug discovery.
What You'll Do
- Multimodal Omics & Functional Genomics
- Design and implement single-cell and spatial omics analyses integrating imaging-, sequencing-, and multiplexed-platform data.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets.
- Perform end-to-end functional genomics analyses (e.g., CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic, proteomic, and pathway-level data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze multi-omics datasets at scale.
- Collaboration with Discovery, Translational & Genetics teams
- Partner with bench and translational scientists to frame questions, design experiments with statistical rigor, and translate computational results into target discovery decisions.
- Interpret statistical genetics outputs and integrate with functional/molecular data for convergent target evidence.
- Develop predictive models combining genetic, functional, and multi-omics evidence to score/rank targets using causal reasoning.
- Contribute to virtual patient/disease modeling to support target validation and translational hypotheses.
- Computational Methods & Platform Development
- Apply modern quantitative methods (Bayesian modeling, causal inference/graphs, mechanistic/agent-based modeling, knowledge graphs, ML/AI for target discovery/scoring) with pragmatic judgment.
- Evaluate and integrate novel AI approaches (graph-based methods, generative models, representation learning, foundation models).
- Influence scalable, reproducible analytical workflows for high-dimensional multimodal integration.
- Influence data architecture, pipeline design, and analytical platform standards with data engineering/infrastructure teams.
- Cross-Functional Influence
- Work with internal AI/statistics teams (and Lilly Research Nucleus) to leverage models and co-develop new approaches for drug discovery.
- Champion standards in analytical rigor, reproducibility, and documentation; advise via code review and collaborative problem-solving.
Qualifications
- Minimum requirements
- Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or a quantitative life science field; training/research combining analytical method development (Bayesian/AI-ML, etc.) with applied work in multi-omics, spatial omics, or functional genomics.
- Preferred
- 2+ years post-doctoral or biopharma/biotech industry experience.
- Experience with spatial omics (spatial transcriptomics, multiplexed imaging, spatial proteomics), single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
- Proficiency in Python and/or R with solid 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/interpret large-scale multimodal datasets and design scalable analytical pipelines.
- Demonstrated experience in at least two: Bayesian methods (e.g., PyMC, Stan), causal modeling, knowledge graphs, ML/AI for biological target discovery, causal inference, or functional genomics at scale.
- Ability to critically interpret statistical genetics outputs (understand what outputs mean and how to use them; not required to run GWAS).
- History of cross-functional collaboration with experimental scientists, statisticians, AI teams, and/or other computational scientists.
- Experience building predictive models or integrative evidence frameworks for target scoring.
- Experience developing/contributing to novel ML/AI models or statistical/algorithmic approaches in a biological context.
- Track record of leading through scientific influence (owning programs, setting technical direction, shaping priorities without direct management authority).
- Strong publication record in peer-reviewed journals or ML/AI venues.
Benefits (explicitly listed)
- Anticipated wage: $166,500 - $266,200.
- Company bonus eligibility (for full-time equivalent employees; depends on company and individual performance).
- Comprehensive benefit program for eligible employees, including: 401(k), pension, vacation, medical/dental/vision/prescription; flexible benefits (e.g., healthcare and/or dependent day care FSA); life insurance/death benefits; certain time off/leave of absence benefits; well-being benefits (e.g., employee assistance program, fitness benefits, employee clubs/activities).
Application instructions (explicitly stated)
- If you require accommodation to submit a resume, complete the accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation.