Senior Scientist, Cellular Genomics

ROLE SUMMARY

We are seeking a highly productive and motivated senior scientist to join the Cellular Genomics group within Pfizer’s Inflammation and Immunology Research Unit. This role is approximately 80% computational biology and 20% wet-lab genomics, with primary accountability for analyzing, integrating, and interpreting high-dimensional single-cell, spatial, and multi-omics datasets that support selected Inflammation and Immunology drug development programs. The individual will apply rigorous computational workflows, statistics, reproducible analysis in R and Python, standard bioinformatics pipelines, and approved agentic computational biology tools to generate decision-relevant biological insight from complex genomic datasets.

The role will also include targeted wet-lab genomic contributions, including study design input, sample-processing strategy, assay-quality review, and limited hands-on support for single-cell, sequencing, or spatial workflows when needed to ensure data quality and interpretability. The candidate should be able to connect disease biology, perturbational responses, pharmacology, and translational context into mechanistic interpretations of drug-candidate effects in collaboration with project scientists, wet-lab genomics experts, translational teams, and computational biology partners.

Independent scientific judgment, strong quantitative reasoning, reproducible computational practice, and practical problem solving in systems biology are required.

• Lead computational analysis of single-cell, spatial, and multi-omics datasets, including quality assessment, preprocessing, statistical analysis, visualization, biological annotation, and reproducible interpretation.
• Translate high-dimensional genomic data into mechanistic hypotheses, biomarker concepts, pharmacology interpretation, and decision-relevant insights for cross-functional drug development teams.
• Apply and adapt established bioinformatics pipelines, statistical workflows, R/Python-based analyses, approved agentic computational tools, and data-management practices in collaboration with internal and external partners.
• Integrate cellular genomics data with disease biology, perturbation biology, pharmacology, translational datasets, and project-specific hypotheses to support mechanism-informed portfolio decisions.
• Provide wet-lab genomics input for study design, sample-collection strategy, assay selection, data-quality requirements, and interpretation of single-cell, sequencing, or spatial profiling workflows.
• Contribute limited hands-on wet-lab execution, troubleshooting, or workflow support when needed to ensure genomic data quality, while maintaining primary focus on computational analysis and biological interpretation.

• BS with 9+ years, MS with 7+ years, or PhD with 0+ years of relevant experience in computational biology, genomics, systems biology, immunology, bioinformatics, or a related discipline
• Strong practical experience analyzing single-cell, spatial, transcriptomic, epigenomic, or other high-dimensional genomic datasets using reproducible computational workflows
• Proficiency in R, Python, statistics, data visualization, quality control, standard bioinformatics pipelines, and interpretation of multi-omics data in biological or pharmacological context
• Hands-on experience of wet-lab genomic assay workflows, including sample processing, sequencing, single-cell, or spatial profiling methods sufficient to evaluate data quality and guide study design

• 2+ years of computational biology or bioinformatics experience analyzing single-cell, spatial, sequencing, or multi-omics datasets in inflammation, immunology, neuroinflammation, or autoimmunity indications
• Experience with version-controlled workflows, command-line tools, scalable data processing, statistical modeling, visualization, and reproducible reporting
• Experience using computational, statistical, machine learning, and approved agentic tools for high-dimensional data analysis, quality review, visualization, interpretation support, or hypothesis generation
• Experience integrating genomic datasets with…