Lead Computational Biologist – Multi-Omics Target Discovery

Initial Therapeutics, Inc.; Boston (MA); On-site; USD 167,000 - 266,000; Full time

Benefits offered by this job

Bonus program

401(k) plan

Comprehensive benefits

Job summary

Lilly is seeking a highly skilled computational biologist to lead analyses of multimodal biological datasets in the CardioMetabolic Research group. You will design end-to-end analyses of spatial and single-cell omics, develop predictive models, and advance causal frameworks to prioritize targets and inform portfolio decisions.

The role involves building scalable analytical pipelines, collaborating with AI teams and biologists, and contributing to novel AI approaches for drug discovery within a

Qualifications

• PhD in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or related field with analytical method development and multi-omics experience.
• 2+ years of post-doctoral or biopharma/biotech industry experience preferred.

Responsibilities

• Design and implement end-to-end analyses of multimodal omics data (spatial & single-cell) to support target discovery.
• Develop scalable pipelines for preprocessing, QC, harmonization, and integration of large datasets.
• Build predictive models combining genetic, functional, and multi-omics data to score targets and distinguish association from mechanism.

Skills

Python

R

Bayesian methods

Causal inference

Multi-omics analysis

Spatial omics

AI/ML fluency

Collaborative teamwork

Education

PhD in computational biology or related

Post-doctoral/Industry experience

Tools

Nextflow

Git

Job description

Lilly is seeking a highly skilled computational biologist to lead analyses of multimodal biological datasets in the CardioMetabolic Research group. You will design end-to-end analyses of spatial and single-cell omics, develop predictive models, and advance causal frameworks to prioritize targets and inform portfolio decisions.

The role involves building scalable analytical pipelines, collaborating with AI teams and biologists, and contributing to novel AI approaches for drug discovery within a