Scientist II, Computational Biology, AI & Multimodal Target Discovery

Scientist II, Computational Biology, AI & Multimodal Target Discovery

JOB DESCRIPTION

Department Data Science, PIRC

Reports to Director of AI in Drug Development

We are seeking a computational biologist to advance target discovery and translational research through integrated multi-omics, agentic AI, computational structural biology, and predictive modeling of cellular perturbations. This individual will build and apply rigorous computational methods that connect human disease evidence to target hypotheses, predict the effects of genetic and pharmacologic interventions, and guide experimental design. The role partners closely with Immunology, Translational Science and Biologics to deliver reproducible analyses, decision-ready evidence, and reusable scientific framework for PIRC's research pipeline.

It's not often you get the chance to make a real impact on the lives of others, while expanding your own possibilities. You'll find that rare opportunity n us, and let's transform lives, together.

Pharma Essentia Corporation is a rapidly growing biopharmaceutical innovator. We are leveraging deep expertise and proven scientific principles to deliver effective new biologics for challenging diseases in the areas of hematology and oncology, with one approved product and a diversifying pipeline. We believe in the potential to improve both health and quality of life for patients with limited options today through the combination of rigorous research and innovative thinking.

Founded in 2003 by a team of Taiwanese-American executives and renowned scientists from U.S. biotechnology and pharmaceutical companies, today we are listed on the Taipei Exchange (TPEx: 6446) and are expanding our global presence with operations in the U.S., Japan, China and Korea, along with a world-class biologics production facility in Taichung.

This role will be in the Pharma Essentia Innovation Research Center (PIRC), the U.S. R&D center for Pharma Essentia. As PIRC is in startup mode with the full backing of Pharma Essentia HQ, this is an exciting time to join, shape computational capabilities, and help grow PIRC from the ground up.

• Integrate and analyze multi-omics datasets, including bulk and single-cell transcriptomics, spatial omics, proteomics, human genetics, functional genomics, and clinical or translational data, to identify disease-relevant cell states, pathways, biomarkers, and therapeutic targets.
• Develop AI agents that support scientific workflows such as literature and knowledge retrieval, data quality assessment, analysis planning, code and tool execution, hypothesis generation, and result synthesis, with appropriate human review, traceability, access controls, and evaluation.
• Develop transparent target-identification and prioritization frameworks that combine genetic evidence, causal inference, knowledge graph, tractability, safety, and translational relevance; clearly communicate evidence strength, uncertainty, and recommended next steps.
• Build, evaluate, and apply virtual-cell and perturbation-response models to predict the effects of genetic and pharmacologic interventions across cell types, disease contexts, doses, and time points; partner with laboratory scientists on prospective validation and model refinement.
• Apply computational structural biology to assess target biology and tractability, including protein structure prediction and quality assessment, variant and domain interpretation, interaction-site analysis, docking, molecular dynamics, and structure-informed hypothesis generation.
• Create reusable, well-maintainable computational frameworks and scalable workflows for data ingestion, harmonization, analysis, model training, and reporting; establish standards for versioning, provenance, quality control, reproducibility, and secure handling of research data.
• Collaborate with Immunology, Biologics, Translational Science, CMC, Clinical, Medical, and other partners to translate program questions into computational plans and deliver actionable results at target nomination and subsequent decision points.
• Track and critically evaluate emerging methods in multimodal foundation models, agentic AI, structural modeling, and computational biology; adopt new approaches when benchmarking shows a clear scientific or operational advantage.
• Communicate methods, findings, limitations, and recommendations through clear visualizations, technical documentation, presentations, and contributions to publications and…