Principal Scientist, Translational Computational Biology

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.

The Oncology Translational IPS team is seeking a Principal Scientist, Translational Computational Biology, to serve as the computational partner to our oncology drug development programs across discovery, translational research, and early clinical development. You will translate patient-derived molecular, spatial, and clinical data into biomarker hypotheses, patient stratification strategies, indication prioritization, pharmacodynamic readouts, and decision-grade recommendations.

The majority of the role is embedded with oncology drug development programs and clinical development teams. The remainder builds computational capability for the broader portfolio: spatial biology, AI-enabled translational science, and reusable analytical methods. The exact emphasis of that capability work will evolve with portfolio priorities and emerging technologies.

This role is for someone who understands drug development, not only data analysis. We are looking for a scientist with a working understanding of the path from target validation and candidate selection through IND-enabling work and early clinical studies (including dose escalation and expansion), and of the strategic role biomarkers play at each stage, who can carry an interpretation into the forum where the decision is actually made.

What you will have to work with

Spatial transcriptomics and spatial proteomics / multiplex immunofluorescence. Dedicated analytical ownership across multiple concurrent oncology programs, on the platforms the team 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 cloud compute alongside a translational informatics team that builds its own methods.

These platforms are already funded and running; this role exists to realize their scientific value.

Clinical and multi-modal patient-derived datasets from BMS's industry-leading early-stage clinical studies in oncology: 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.

Your contributions will influence development strategies and play a vital role in propelling the BMS early-stage oncology pipeline forward, directly impacting the treatment of cancer patients.

You will apply these data across two areas:

• Oncology drug development program, translational, and early clinical development support. The majority of the role. Biomarker strategy; patient selection and stratification; indication prioritization; target validation; IND-enabling and early clinical trial interpretation; data-driven recommendations for program decisions.
• Computational innovation and portfolio capability. The remainder. Spatial biology; AI-enabled translational science; multimodal integration; reusable workflows, automation, and scalable analytical methods that serve the portfolio rather than a single program.

Key Responsibilities

• Oncology drug development program, translational, and early clinical development support
• Oncology program partnership. Serve as the translational computational scientist for assigned oncology drug development programs across the discovery-to-early-clinical continuum, from target validation through early clinical studies.
• Biomarker and patient strategy. Shape biomarker strategy, patient selection and stratification hypotheses, pharmacodynamic marker plans, indication prioritization, and enrichment approaches.
• Patient-derived data analysis. Analyze and integrate multimodal molecular, clinical, and translational datasets from oncology studies, including bulk and single-cell RNA-seq, ctDNA, WES, and liquid biopsy, TCR-seq, flow cytometry, cytokine profiling, IHC, proteomics, and spatial readouts.
• Discovery-to-translational support. Use patient molecular data, causal and driver inference, regulatory network analysis, perturbation readouts, and orthogonal evidence to support target nomination, validation, candidate selection, and IND-enabling decisions.
• Decision-grade communication. Translate complex multimodal analyses into clear, decision-grade biological narratives. Every result ships with an interpretation, its limitations, and a recommendation, and you carry that recommendation to the program team, translational review, or governance forum where the decision is made.
• Computational innovation and portfolio capability
• Spatial biology. Take dedicated analytical ownership of spatial data across the portfolio: spatial transcriptomics (Xenium, Visium, Visium HD) and spatial proteomics / multiplex immunofluorescence (e.g., COMET, PhenoCycler), realizing the scientific value of the atlas, platform, and vendor investments already committed. You will partner with digital pathology and image-analysis colleagues on H&E whole-slide analysis; deep prior digital pathology experience is welcome but not required.
• AI-enabled translational science. This is an explicit mandate of the role, not a side project. Design and deploy AI approaches for evidence integration and hypothesis generation across patient omics, genetic evidence, perturbation data, and the literature, including LLM-based extraction, agentic and multi-step workflows, and emerging biological foundation models. Given strategic direction, you will have the autonomy to scope, build, and deploy the methods that become the team's translational decision infrastructure. You should not just run existing tools; we want someone who sees what is missing from current approaches and builds it.
• Reusable methods and automation. Build reproducible workflows and cloud-ready pipelines for multimodal data (single-cell, CRISPR and Perturb-seq screens, spatial), so capability persists as a team asset rather than as one-off analyses.
• Scientific influence. Mentor junior scientists and interns, document methods to publication-quality standards, and help raise the computational maturity of the broader translational organization.
• Basic Qualifications
• Ph.D. in computational biology, bioinformatics, biostatistics, statistics, human genetics, computer science, or a related quantitative field, with 4+ years of relevant academic and/or industry experience; Or Master's Degree with 6+ years; Or Bachelor's Degree with 8+ years.

Preferred Qualifications

• We do not expect any one candidate to bring all of the following. Depth in several of these areas, combined with the judgment to know which question a program is actually asking, matters more than breadth across all of them.
• Demonstrated experience analyzing, integrating, and interpreting high-dimensional patient-derived molecular data in oncology or another translational disease area.
• Strong programming skills in R and/or Python, with practical experience in reproducible analysis and data visualization.
• Working knowledge of the oncology drug development process, sufficient to anticipate what a program needs at target validation, candidate selection, IND-enabling work, and early clinical development.
• Clear scientific communication and the ability to collaborate effectively with biology, translational medicine, clinical development, statistics, and quantitative science partners.
• Spatial biology: hands-on experience with spatial transcriptomics (e.g., Xenium, Visium, Visium HD, CosMx) and/or spatial proteomics and multiplex immunofluorescence (e.g., COMET, PhenoCycler), including cell segmentation, phenotyping, and neighborhood or spatial statistics; familiarity with the analysis stack (Squidpy, SpatialData, scverse) and with digital pathology tooling (HALO, QuPath).
• Prior experience in oncology drug development at a biopharmaceutical company, in translational sciences, discovery, or early clinical development.
• Experience with biomarker strategy, patient selection, pharmacodynamic readouts, companion diagnostic (CDx) development, or clinical translational data interpretation.
• AI and machine learning applied to translational problems: LLM-based evidence and literature extraction, agentic or multi-step analytical workflows, biological foundation models, or multimodal representation learning.
• Causal and driver inference, regulatory network analysis, or other approaches that nominate and prioritize targets from patient molecular data.
• Perturbation biology and functional genomics: CRISPR screens, Perturb-seq, and genetic validation in patient-derived model systems, including integrating perturbation readouts against patient data.
• Cell-type inference and gene-expression deconvolution from bulk, single-cell, and spatial data.
• Reproducible engineering practice: workflow managers (e.g., Nextflow, Snakemake), version control (Git), high-performance computing, and cloud platforms.
• A record of methods development evidenced by peer-reviewed publications and, ideally, released open-source tools or packages.
• A collaborative problem-solver who can operate in ambiguous program settings and translate complex computational output into practical recommendations.
• We hire for skills and capabilities, not just credentials – if this role excites you, but doesn’t perfectly match your resume, we encourage you to apply anyway.