Principal Scientist - Computational Cancer Immunology

Responsibilities

• Serve as the subject matter expert and hands-on analyst for single cell and spatial transcriptomics data supporting discovery cancer immunology programs.
• Design and execute end-to-end analyses of single cell and single nucleus RNA-seq data, including cell type and cell state annotation, differential abundance and expression, and cell-cell communication analysis.
• Analyze spatial transcriptomics data across sequencing- and imaging-based platforms, including deconvolution, cell typing, and spatial neighborhood and niche analysis.
• Integrate single cell and spatial readouts with complementary modalities — bulk RNA-seq, high throughput proteomics, multiplex flow cytometry, genomics, and clinical data — to build holistic translational data packages for target concept opportunities.
• Support cancer immunology discovery programs from target identification through candidate selection, partnering with program teams on target validation, mechanism of action, and translational hypotheses generated from pre-clinical models and patient-derived samples.
• Partner with biologists and translational teams upstream of data generation to shape experimental design, including platform and panel selection, sample and cohort composition, controls, and powering.
• Develop and maintain reproducible, well-documented, and reusable analysis workflows, contributing to shared codebases, pipelines, and analytical standards that benefit the broader IPS organization.
• Evaluate, benchmark, and where needed extend emerging computational methods in single cell and spatial biology, and advocate for their adoption where they create scientific advantage.
• Communicate findings and recommend follow-up actions in multiple settings (including 1:1, seminars, and team meetings).
• Participate in authorship of scientific reports, patents, and publications, and present methods and conclusions to publishable standards.
• Provide scientific mentorship and technical guidance to junior computational scientists and interns, and influence cross-functional matrix teams without formal reporting authority.

Qualifications

Basic qualifications: Bachelor's degree with 8+ years of academic/industry experience, Or Master's degree with 6+ years of experience, Or PhD with 4+ years of experience in computational biology, bioinformatics, computational cancer immunology, or a related field.

Preferred qualifications: PhD from a recognized institution in a quantitative field such as computational biology, bioinformatics, computational cancer immunology, statistics, or related. Demonstrated depth in single cell transcriptomics, with a track record of independently delivering scRNA-seq analyses from raw data through biological interpretation. Hands-on experience with spatial transcriptomics data from one or more commercial platforms. Experience analyzing and integrating other high-dimensional molecular datasets, such as multi-omics (e.g., bulk RNA-seq, proteomics), flow cytometry, genome-wide genotyping array, or clinical data. Advanced hands-on knowledge of at least one high-level programming language such as R or Python, with fluency in the associated single cell and spatial ecosystems (e.g., Seurat, Bioconductor, Scanpy, squidpy) and with reproducible research practices including version control and workflow management. Experience applying machine learning and AI methods to high-dimensional biological data — for example deep learning models of cell state, single cell foundation models, or generative approaches — together with the judgment to know when they offer advantage over established statistical methods. Familiarity with computational pathology approaches and multimodal histology–transcriptomics models that predict molecular profiles from H&E images, and interest in applying them alongside spatial transcriptomics. Fluency with AI-assisted development tools, such as LLM-based coding assistants and agentic workflows, to accelerate analysis and software development, applied with appropriate scientific rigor and validation of outputs. Strong statistical foundation, including experience with experimental design and with the analysis of nested, repeated-measures, or otherwise non-independent data. Experience working in cloud/high performance computing environments on large-scale datasets. Track record (such as scientific publications) in driving and advancing research projects/programs with computational approaches. Experience supporting drug discovery programs, with an understanding of how computational findings inform target identification, validation, and candidate progression decisions. Demonstrated ability to influence, align, and deliver within cross-functional matrix teams without formal authority. Strong oral and written communication skills, including the ability to convey complex quantitative results to non-computational audiences. Background in cancer immunology, tumor microenvironment biology, T cell engagers, or drug discovery preferred.