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Principal Scientist, AI-Driven Oncology Target Discovery

Job in Cambridge, Middlesex County, Massachusetts, 02140, USA
Listing for: Bristol Myers Squibb
Full Time position
Listed on 2026-07-01
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Data Scientist, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 166770 - 202086 USD Yearly USD 166770.00 202086.00 YEAR
Job Description & How to Apply Below

Principal Scientist, AI-Driven Oncology Target Discovery

The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning—across the full lifecycle of drug discovery and development and across all therapeutic areas  do this in close partnership with scientific and clinical experts in the field, both inside and outside the company.

We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers.

Here, you'll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You'll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma.

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.

Key Responsibilities

  • 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.

Basic Qualifications

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

Preferred Qualifications

  • 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.

If you come across a role that intrigues you but doesn't perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.

Compensation Overview:

Cambridge Crossing: $166,770 - $202,086

The…

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