Principal Scientist, Translational AI Foundations

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. When you join BMS, you are joining a high-achieving team united by a common mission. 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 at BMS. We 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. Position Summary Translational IPS is forming Translational AI Foundations (TAIF), a small team of computational biologists and AI engineers that works side by side with our translational research groups. TAIF turns the biomarker analyses scientists already perform by hand into validated, reusable AI agents and workflows, then transfers each one to the research group that will use and maintain it. The science stays with those groups; TAIF builds the shared tools that make it faster and reproducible. We are seeking a Principal Scientist to design, build and validate the team's assay-level AI agents and the analysis logic behind them, so that a biomarker analysis that takes weeks of hands-on work today can be run, reviewed and carried to a recommendation in days. The initial focus is spatial transcriptomics and H&E, followed by other data types (ctDNA, IHC, cytokine panels, RNA-seq, PK/PD and clinical data). You will work with real program data and program scientists, and spend part of your time working directly with translational research groups. You will not be assigned to a single drug program: the output of this role is a validated, reusable tool, not a one-off analysis. This role is for someone who has done these analyses by hand and knows where they break: a computational biologist with deep spatial and single-cell expertise who has already built LLM-based or agentic analysis workflows around that expertise, and who can show, with evidence, whether an agent's output is supported by the data or merely well-formed. You will automate established analyses with AI agents and hand them to the research groups that use them. What you will have to work with Spatial transcriptomics and spatial proteomics / multiplex immunofluorescence. The platforms the organization runs today: Xenium, Visium and Visium HD for spatial transcriptomics, and platforms such as COMET or PhenoCycler for spatial proteomics / multiplex immunofluorescence, backed by a pan-cancer spatial atlas license, an H&E-to-mIF platform partnership, and digital pathology and image-analysis colleagues for whole-slide H&E. These platforms are funded and running; this role exists to make their analysis reproducible and scalable. Clinical and multimodal patient-derived datasets from BMS's early-phase clinical studies: the molecular and clinical biomarker data generated by our own early-phase trials, spanning RNA-seq, ctDNA, WES, TCR-seq and CTC, together with flow cytometry, cytokine profiling, IHC and proteomics, with the PK/PD and clinical data alongside them. A shared engineering platform. A platform engineering scientist on the same team maintains the shared tooling: templates, automated tests, release checks, a traceable record behind every number, and the connections to governed data (Databricks), enterprise AI services and AWS cloud compute. You set the scientific standard that platform enforces; you do not have to build the plumbing. Subject-matter experts in every assay, and designated AI contacts in each translational research group who co-develop tools with you, review them, and take them over once they are validated. Your contributions will speed translational decisions across the BMS early clinical portfolio and help bring new medicines to patients sooner. You will apply these resources across two areas: • Assay agents, their expert specifications and their evaluation. The majority of the role: building the agents, writing the specification an expert signs off on, and setting the test cases and pass criteria. • Delivery with the translational groups. The remainder: the first spatial pilots, then direct work with research groups, and maintaining each agent's validation tests after hand-off. Key Responsibilities Assay agents, specifications and evaluation • Assay agents. Build and maintain AI agents for the major biomarker data types (spatial and H&E first, then ctDNA with the platform engineering scientist, IHC, cytokine, RNA-seq, PK/PD and clinical data), each with defined inputs and outputs, a traceable source for every number and a human-review step. Each agent automates an analysis a colleague already does by hand. • Expert specifications and evaluation. Write each agent's specification with the organization's