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.

This is an individual contributor role in Boston or Indianapolis for an 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 Cardio Metabolic 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 work streams. In a team setting they 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,andcausal 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 toleverageand co-develop models for drug discovery andwillrepresentcomputational innovation with CMR and across the broader organization.

This role suits a scientist who combines depth in computation withthe independence to drive programs and the collaborative instinct to elevate the work of those around them.

Someone wholoveshands-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 intoCMRscience. Ideally, they are also motivated to build novel AImodels themselves to advance drug discovery.

Above all, theywant to be part of a teammotivatedtobuilda robust platform together.

• Design and implement single cell and spatial omics analyses integrating imaging-based, sequencing-based, and…