Principal Scientist, AI-Driven Oncology Target Discovery

Summary

We are seeking a highly motivated and innovative Principal Computational Scientist at the intersection of machine learning, agentic AI, and oncology discovery to drive the identification of novel therapeutic targets and resistance mechanisms. In this role, you will integrate multimodal biological, clinical, and real‑world datasets to generate actionable insights for target nomination, validation, and portfolio strategy. In partnership with cross‑functional teams, you will design and scale advanced analytical platforms and agent‑based systems that accelerate decision‑making and enhance translational impact across discovery pipelines.

• Drive integration of computational approaches into cross‑functional target discovery workflows, partnering with functional genomics, discovery biology, and computational biology teams to inform target nomination and portfolio decisions.
• Lead the design and deployment of agentic AI and machine learning systems to enable scalable, end‑to‑end workflows for target identification, prioritization, and validation.
• Integrate and analyze multimodal, patient‑derived datasets (omics, functional screens, real‑world data) to uncover disease biology, resistance mechanisms, and patient stratification opportunities that inform target selection.
• Translate complex analytical outputs into clear, decision‑ready insights to support Go/No‑Go recommendations and guide target discovery strategy.
• Author scientific reports and present methods, results, and conclusions to a publishable standard.

• Bachelor's Degree with 8+ years of academic/industry experience
• Master's Degree with 6+ years of academic/industry experience
• PhD with 4+ years of academic/industry experience

• Education Ph.D. in Computational Biology, Systems Biology, Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Deep expertise in computational target identification approaches, including functional genomics, perturbation screens, single‑cell and spatial omics, and real‑world patient data.
• Demonstrated experience developing and deploying applied AI agents and/or complex LLM‑driven applications in research or industry settings.
• Excellent collaboration and leadership skills, with a track record of influencing cross‑functional teams and operating effectively in fast‑paced, ambiguous environments.
• Strong grounding in systems biology and disease biology within drug discovery, preferably in oncology, with experience translating computational findings into therapeutic hypotheses.
• Proven scientific impact, with a strong publication record in computational biology, machine learning, or bioinformatics, and the ability to communicate complex methodologies and insights effectively.

Cambridge Crossing $166,770 - $202,086. The starting compensation range(s) for this role are listed above for a full‑time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. Final, individual compensation will be decided based on demonstrated experience.

Benefit offerings are subject to the terms and conditions of the applicable plans in effect at the time and may require enrollment. Our benefits include:

• Health Coverage:
  Medical, pharmacy, dental, and vision care.
• Wellbeing Support Programs: BMS Well‑Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
• Financial Well‑being and Protection: 401(k) plan, short‑ and long‑term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments, and ongoing support in their roles. Applicants can request accommodations prior to accepting a job offer. Visit  for our complete Equal Employment Opportunity statement.