Principal Scientist, Discovery BioSciences – Integrative Targets, Oncology

Position Summary

We are seeking a highly motivated and innovative AI/ML scientist with experimental cancer biology experience to lead efforts at the intersection of agentic AI, machine learning, functional genomics, and target discovery. In this role, you will identify, create, and validate datasets, and contribute to the development of advanced algorithms and agentic-based tools to discover new therapeutic options within oncology. In close partnership with cross‑functional teams, you will integrate outputs yielded from functional screens, diverse ‘omics, and real‑world data to identify novel actionable insights for testing.

This role offers a unique opportunity to transform target identification by enhancing predictive and analytical capabilities to meaningfully accelerate oncology target discovery and improve patient outcomes.

• Drive high‑quality data science that is grounded in a deep understanding of biology/mechanisms by contributing, developing, and pressure‑testing applied data science methodologies and approaches to identify new targets for oncology drug development.
• Co‑develop, communicate, and execute a multi‑disciplinary research strategy to enable and enhance innovation in the identification of oncology new targets by incorporating identified datasets and validating hypotheses yielded from AI/ML approaches.
• Closely align with the Informatics and Predictive Science teams to drive a portfolio of data‑science‑driven integrative New Target projects for oncology, and drive collaborative innovation to inform large and small molecule inhibitor target modalities.
• Collaborate on the development and implementation of innovative machine learning algorithms, AI/foundation models, and platforms to enable the delivery and validation of novel target insights with the BMS IPS, AI/ML, and other technology‑focused teams.
• Play a leading role in matrix teams centered around key technologies such as new leads and computational chemistry, genomics, proteomics, and spatial technologies.

• Bachelor’s degree with 8+ years of academic and/or industry experience.
• Master’s degree with 6+ years of academic and/or industry experience.
• Ph.D. or equivalent advanced degree in the life sciences with 4+ years of academic and/or industry experience.

• Position located at the Cambridge, MA site; remote location not available.
• Ph.D. with 4+ years of experience or M.S. with 6+ years of experience in cancer biology with a strong scientific mindset and a solid foundation for applying computational biology, systems biology, and statistical approaches.
• Strong understanding of analytical and computational approaches and methodologies for functional genomics, single‑cell and spatial ‘omics.
• Broad experience with generating and analyzing genomics, proteomics data and/or functional genomics datasets.
• Experience executing target identification strategies including design, implementation, and validation of targets from forward and reverse genetic and/or phenotypic screens using genome engineering techniques, molecular biology, and genetic perturbation (e.g., shRNA, CRISPR, degron tagging of endogenous loci).
• Demonstrated expertise in uncovering mechanistic biology that best informs target modality for drug discovery efforts.
• Excellent communication and cross‑collaborative skills, with the ability to operate effectively in fast‑paced research environments and influence diverse stakeholders.
• Strong understanding of functional oncology targets such as mutated oncogenes and principles around identifying cell surface targets for modalities such as antibody‑drug conjugates, T‑cell engagers, multispecifics, and radioligand therapies.
• Proven history of contributions to the scientific community in the form of papers and/or conference presentations.
• Experience in applying AI/machine learning and statistical modeling techniques.

Cambridge Crossing: $156,180 - $189,252. The starting compensation range for this role is listed above. Additional incentive cash and stock opportunities (based on eligibility) may be available. Final, individual compensation will be decided based on demonstrated experience.