Principal Scientist – Computational Biology & Translational AI (Oncology Precision Medicine)

Role Summary

The Principal Scientist – Computational Biology & Translational AI (Oncology Precision Medicine) will lead pan-asset forward and reverse translational analyses, designing and deploying AI-enabled analytical and agentic systems that connect preclinical, translational, and clinical data to drive mechanistic understanding, biomarker hypotheses, and development strategy across oncology programs. Based in the Computational Biology group within the Center for Technology and Innovation (CfTI) in Amgen's Precision Medicine organization, the role focuses on modern AI (Generative AI and Agentic AI) and multi-modal data integration to accelerate translational insight. You will partner with biology, assay, pathology, and clinical teams to contextualize translational signals, communicate results to stakeholders, and contribute as a translational science and AI thought leader across assets.

Responsibilities

• Lead pan‑asset forward and reverse translational analyses, integrating preclinical, translational, and clinical data to generate mechanistic insight, biomarker hypotheses, and development‑relevant evidence.
• Design and deploy Generative AI and Agentic AI systems that support hypothesis generation, evidence synthesis, and cross‑modal reasoning across discovery, translation, and clinical development.
• Develop multi‑agent, tool‑using AI workflows that integrate structured and unstructured data (omics, imaging, pathology, clinical data, literature) to accelerate translational insight generation.
• Apply modern statistical and AI‑enabled approaches to connect molecular mechanisms, biomarkers, and clinical phenotypes, supporting indication strategy, patient stratification concepts, and learning across assets.
• Drive reverse translation by systematically linking clinical observations back to molecular and biological hypotheses using multimodal data and AI‑assisted reasoning frameworks.
• Analyze and integrate multi‑modal translational data (e.g., genomics/transcriptomics, proteomics, epigenomics, single‑cell, imaging, pathology, clinical endpoints) to support forward and reverse translational learning across oncology assets.
• Build and maintain AI‑enabled, reproducible translational analysis pipelines, including integration with agentic systems and automated insight‑generation workflows.
• Partner with biology, assay, pathology, and clinical teams to contextualize and interpret translational signals, rather than owning routine assay delivery.
• Represent CfTI in Amgen program and portfolio teams as a translational science and AI thought leader, contributing AI‑enabled forward and reverse translational insight across assets.
• Communicate results clearly to clinical and scientific stakeholders and contribute to translational and biomarker strategy, regulatory‑facing analyses (as needed), and reverse translation learning.

Qualifications

• Required: Doctorate degree, PhD, PharmD or MD and 2 years of computational Biology experience
• Required: Master’s degree and 5 years of computational Biology experience
• Required: Bachelor’s degree and 7 years of computational Biology experience
• Preferred: PhD in Bioinformatics, Mathematics, Statistics, Computer Science, Computational Biology, Data Science, or related field, with a strong foundation in biology and translational science.
• Preferred: Demonstrated expertise in forward and/or reverse translational science, linking molecular mechanisms, biomarkers, and clinical outcomes across discovery and development.
• Preferred: Hands‑on experience developing Generative AI and/or Agentic AI systems applied to scientific reasoning, hypothesis generation, or evidence synthesis.
• Preferred: Experience integrating multi‑modal data (omics, imaging, pathology, clinical, text/literature) using AI‑enabled or model‑based approaches.
• Preferred: Strong understanding of AI system evaluation, interpretability, and scientific reliability in decision‑critical environments.
• Preferred: Working knowledge of clinical biomarker platforms and translational readouts, enabling effective collaboration with assay and clinical teams.
• Preferred: Demonstrated experience generating translational and biomarker insights that influenced clinical development decisions (e.g., indication strategy, trial design, stratification, or mechanistic understanding).
• Preferred: Strong programming experience in R and/or Python, with experience integrating AI/LLM‑driven components into reproducible analysis workflows (version control, workflow orchestration, documentation).
• Preferred: Familiarity with modern data and analytics infrastructure supporting scalable, auditable AI systems in clinical research environments.
• Preferred: Ability to work effectively in a highly matrixed environment and drive scientific and technical innovation collaboratively across functions.
• Preferred: Strong written and oral communication skills, self‑motivation, independence, and scientific leadership.