Principal Scientist, Translational Computational Biology
About The Position
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.
Requirements
- 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.
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
- Shape biomarker strategy, patient selection and stratification hypotheses, pharmacodynamic marker plans, indication prioritization, and enrichment approaches.
- 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.
- 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.
- 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.
- 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.
- Partner with digital pathology and image-analysis colleagues on H&E whole-slide analysis; deep prior digital pathology experience is welcome but not required.
- 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.
- 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.
- Mentor junior scientists and interns, document methods to publication-quality standards, and help raise the computational maturity of the broader translational organization.
Benefits
- wellbeing support
- retirement and financial protection benefits
- insurance offerings (medical, dental, vision, life and disability)
- Flexible Time Off (FTO)
- 11 paid company holidays each year
- 160 hours of paid vacation annually for new hires
- 3 optional holidays
- paid sick leave
- up to two paid volunteer days per year
- summer hours flexibility
- leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs
- annual Global Shutdown between Christmas Day and New Year's Day
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
Ph.D. or professional degree
