Senior Scientist, Oncology Bioscience – AI‑Enabled Discovery 

This role sits at the intersection of oncology bioscience and applied agentic AI/Data Science. The Senior Scientist will embed AI agents/Data Science tools into discovery workflows to accelerate hypothesis generation, experimental design, data integration, and decision‑making, while sustaining strong biological rigor through hands‑on experimental execution and interpretation. This is not a data‑science‑only role; success requires biological understanding combined with practical AI deployment to drive meaningful oncology discovery outcomes. 

Key Responsibilities 

AI‑Enabled Discovery  

• Apply agentic AI systems to develop AI agent to address complex oncology R&D questions and/or facilitate lab automation process.  

• Use AI platforms (e.g., Claude code, Co‑Scientist) for integrated analysis of phenotypic, signaling, and multi‑omics data, hypothesis generation, and literature synthesis.  

• Translate AI‑driven insights into actionable discovery decisions (target validation, LoS positioning, combination strategies).  

• Clearly communicate AI insights, linking reasoning to biological evidence and program impact. 

Oncology Bioscience & Experimental Execution 

• Design, execute, and interpret in vitro oncology assays to test AI‑generated hypotheses.  

• Perform cell‑based functional studies assessing candidate drug activity.  

• Provide biological validation and critical interpretation of AI‑driven predictions.  

• Partner cross‑functionally to ensure scientific rigor and relevance.  

Basic Qualifications 

• BS with 3+ years, MS with 5+ years, or PhD with 1+ years of experience in Oncology, Biology, Data Science, or a related field. 

• Experience developing or applying agentic AI systems to scientific or R&D problems. 

• Experience using agentic AI platforms for hypothesis generation, data interpretation, or literature synthesis. 

• 2+ years of hands‑on experience executing and/or overseeing in vitro tumor cell biology studies. 

• Expertise in integrated omics data analysis and biological interpretation. 

• Strong data reporting and communication skills aligned to discovery decision‑making. 

Preferred Qualifications 

• Basic coding skills in Python or R. 

• Strong collaboration and problem‑solving skills in cross‑functional teams. 

• Comfort operating in fast‑moving environments integrating biology, AI, and automation