Computational Biologist: AI-Powered Target Discovery

PharmaEssentia Corporation; Bedford (MA); On-site; USD 130,000 - 180,000; Full time

Job summary

PharmaEssentia Corporation seeks a Scientist II, Computational Biology to advance target discovery through multi-omics, agentic AI, and predictive modeling of cellular perturbations. You will build rigorous computational methods connecting disease evidence to targets and guide experimental design in collaboration with Immunology, Translational Science and Biologics.

The role emphasizes reproducible analyses, program-wide frameworks, and communication of evidence strength and uncertainty to drive

Qualifications

• Ph.D. in a quantitative field with 0–3 years industry experience.
• Experience in multi-omics target discovery, AI agent development, or computational biology for drug discovery.
• Proficiency with ML/AI methods for biological data and clear communication of methods.

Responsibilities

• Integrate and analyze multi-omics datasets to identify disease-relevant targets.
• Develop AI agents to support scientific workflows with human oversight.
• Build transparent target-identification frameworks combining genetic evidence and translational relevance.
• Develop models to predict effects of interventions across cell types and disease contexts.
• Apply structural biology approaches to assess target tractability and generate hypotheses.

Skills

multi-omics target discovery

AI agent development

computational structural biology

cellular perturbation modeling

Python

R

Education

Ph.D. in Computational Biology, Bioinformatics, Computer Science, or related quantitative discipline

Tools

Cloud computing

Docker

Kubernetes

Workflow engines

Job description

PharmaEssentia Corporation seeks a Scientist II, Computational Biology to advance target discovery through multi-omics, agentic AI, and predictive modeling of cellular perturbations. You will build rigorous computational methods connecting disease evidence to targets and guide experimental design in collaboration with Immunology, Translational Science and Biologics.

The role emphasizes reproducible analyses, program-wide frameworks, and communication of evidence strength and uncertainty to drive