Senior Scientist, Oncology Bioscience – AI‑Enabled Discovery Full-time

Senior Scientist

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

• Apply agentic AI systems to develop AI agents 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.