Computational Biologist - Quantitative Methods & Target Discovery

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life‑changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first.

We’re looking for people who are determined to make life better for people around the world.

This is an 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. The role sits 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, 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 within 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.

Someone who loves hands‑on computational work and holds strong, experience‑driven experience 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.

• 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 work streams (CRISPR screens, perturb‑seq, high‑content perturbation readouts) and integrate results with transcriptomic, proteomic, and pathway‑level data for target prioritization
• Ingest,…