Computational Biologist - Quantitative Methods & Target Discovery

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. The Opportunity This is a n individual contributor role in Boston or Indianapolis for a n experienced computational biologist who will lead analyses of multimodal biological datasets and develop methods that advance target discovery in cardiometabolic diseases. The role, in the Data Science team in CardioMetabolic Research (CMR) at the intersection of spatial and single-cell omics, causal inference, AI/ML, and functional genomics. The scientist in this role will independently design and implement end-to-end analyses of spatial and single-cell transcriptomic, proteomic, and metabolomic datasets, as well as functional genomics workstreams. In a team setting t hey will integrate results across modalities and with genetic evidence to build convergent frameworks for target prioritization, and develop predictive models to score targets, distinguish association from mechanism, and provide measures of confidence that inform portfolio decisions. The role also involves advancing the team's quantitative toolkit — introducing ML/AI approaches , knowledge graphs, Bayesian methods, and causal modeling where they contribute — and influencing the data architecture and analytical standards that support reproducible, scalable science. The scientist will collaborate with internal AI teams, data engineering teams, translational biology teams, statistical geneticists, and statisticians to leverage and co-develop models for drug discovery and will represent computational innovation with CMR and across the broader organization. This role suits a scientist who combines depth in computation with the independence to drive programs and the collaborative instinct to elevate the work of those around them. Who we are looking for Someone who loves hands-on computational work and holds strong, experience-driven experience opinions on methods. A scientist who leads through scientific influence : advising colleagues, raising analytical standards, and improving the science around them. The right candidate is drawn to connecting genetic evidence, public multi-omics data, and experimental model data to functional biology — building causal frameworks around targets and delivering measures of confidence and uncertainty that inform decisions on targets and molecules. They collaborate well with statisticians — adapting methods from other domains, co-developing new approaches , or stress-testing an existing framework to find where it breaks. They are pragmatic about methods: they know when a Bayesian model is worth the investment and when a simpler approach will do. They have enough AI and ML fluency — from agentic systems for routine tasks to foundation models and graph neural networks for complex problems — to work productively with AI teams and translate those capabilities into CMR science. Ideally, they are also motivated to build novel AI models themselves to advance drug discovery. Above all, they want to be part of a team motivated to build a robust platform together. 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 as well as 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 Hands-on end-to-end analysis of functional genomics workstreams (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 closely with pre-clinical bench scientists and translational biologists in CMR to frame questions, design experiments with statistical rigor, and translate computational results into target discovery decisions Consume and interpret outputs from statistical genetics and integrate them with functional and molecular data to build convergent evidence frameworks for target nomination Develop predictive models that combine 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 where multi-omics and mechanistic evidence converge to support target validation and translational hypotheses Computational Methods & Platform Development Apply and introduce modern quantitative methods — Bayesian modeling, causal inference and causal graph modeling, mechanistic or agent-based modeling, knowledge graphs, ML/AI for