Computational Biologist - Quantitative Methods & Target Discovery

Computational Biologist - Quantitative Methods & Target Discovery at Eli Lilly. Boston, MA.

Individual contributor role for an experienced computational biologist who will lead analyses of multimodal biological datasets and develop methods that advance target discovery in cardiometabolic disease. Sits at the 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 tissue architecture, cellular neighborhoods, and system-level dynamics
• Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets
• End-to-end analysis of functional genomics workstreams (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic, proteomic, and pathway-level data for target prioritization
• 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:

• Partner with pre-clinical bench scientists and translational biologists to frame questions, design experiments, and translate computational results into target discovery decisions
• Integrate statistical genetics outputs 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

Computational Methods & Platform Development

• Apply modern quantitative methods — Bayesian modeling, causal inference, knowledge graphs, ML/AI for target discovery and scoring
• Evaluate and integrate novel AI approaches including graph-based methods, generative models, representation learning, and foundation models
• Influence data architecture, pipeline design, and analytical platform standards

Requirements

• PhD in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or quantitative life science field

Preferred

• 2+ years post-doctoral or biopharma/biotech industry experience
• Experience with spatial omics platforms, single-cell RNA-seq, proteomics, metabolomics, or multi-omics data integration
• Proficiency in Python and/or R with solid software practices
• Familiarity with workflow orchestration (Nextflow), cloud-native environments
• Experience in at least two of: Bayesian methods (PyMC, Stan), causal modeling, knowledge graphs, ML/AI for biological target discovery, functional genomics at scale
• Ability to interpret statistical genetics outputs and integrate with molecular data
• Track record of leading through scientific influence
• Strong publication record in computational biology, multi-omics, or applied ML