Computational Biologist - Quantitative Methods & Target Discovery

The Opportunity (Individual Contributor; Boston or Indianapolis)
Responsibilities:

• Independently design and implement end-to-end analyses of spatial and single-cell transcriptomic, proteomic, and metabolomic datasets, plus functional genomics work streams.
• Integrate results across modalities and with genetic evidence to build convergent frameworks for target prioritization.
• Develop predictive models to score targets, distinguish association from mechanism, and provide confidence measures to inform portfolio decisions.
• Advance quantitative toolkit: introduce ML/AI, knowledge graphs, Bayesian methods, and causal modeling where applicable.
• Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets.
• Perform hands-on functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic/proteomic/pathway data for prioritization.
• Collaborate cross-functionally to frame questions, translate computational outputs into discovery decisions, and co-develop models for drug discovery.
• Champion standards for analytical rigor, reproducibility, and documentation; advise peers through reviews and shared problem-solving.

• Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or quantitative life science; training/research combining analytical method development (Bayesian approaches, AI/ML, etc.) with applied multi-omics, spatial omics, or functional genomics.

• 2+ years post-doc or biopharma/biotech experience.
• Experience with spatial omics, single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
• Proficiency in Python and/or R; solid software practices and scientific computing libraries.
• Familiarity with workflow orchestration (e.g., Nextflow) and cloud-native environments.
• Experience with at least two:
  Bayesian methods, causal modeling, knowledge graphs, ML/AI for target discovery, causal inference, or large-scale functional genomics.

• Company bonus (company and individual performance dependent).
• Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance/death benefits, time off/leave, and well-being benefits.