Computational Biologist - Quantitative Methods & Target Discovery

The Opportunity 

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 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 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 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.