Senior Scientist II, Bioinformatics (Spatial Proteomics and Single Cell Omics)

Responsibilities

• Perform advanced analysis of high-dimensional proteomics and spatial datasets, including CyTOF, Flow Cytometry, and IMC to generate actionable biological insights.
• Develop, optimize, and maintain computational workflows and analytical pipelines for the processing, visualization, and interpretation of spatial proteomics and multi-omics data.
• Conduct image-based protein quantification using IMC and spatial technologies, with a focus on cellular localization, tissue architecture, and co-localization of immune and stromal components.
• Apply machine learning and AI-based approaches and network dynamics to identify patterns, classify disease states, prioritize biomarkers, and support target discovery across integrated multi-omics datasets.
• Collaborate with immunologists, biologists, computational scientists, and data engineers to translate analytical findings into preclinical and translational research applications.
• Integrate proteomics data with transcriptomic, spatial, and other omics modalities to build a more comprehensive view of disease mechanisms.
• Communicate complex analytical methods, results, and biological interpretations to both technical and non-technical stakeholders.
• Contribute to scientific publications, conference presentations, and other internal or external research deliverables as needed.
• Education:
• BS, MS, or PhD with typically 12+ (BS), 10+ (MS), or 4+ (PhD) years of experience in bioinformatics, computational biology, systems immunology, proteomics, data science, or a related field.
• Technical Expertise:
• Proficiency in R and Python, with experience working in Unix/Linux environments.
• Strong foundation in statistical analysis, data modeling, machine learning, and AI methods applicable to high-dimensional biological data.
• Experienced in CyTOF, flow cytometry, and single-cell omics data analysis.
• Skilled in handling large-scale proteomics and multi-omics datasets, including quality control, normalization, integration, feature engineering, and downstream analysis.
• Familiarity with and image-based computational workflows, spatial analysis, network dynamics (Geneformer) and histology.
• Preferred Skills:
• Experience with computational methods for protein quantification, post-translational modification analysis, and protein-protein interaction network analysis.
• Familiarity with multi-omics integration approaches, including proteomics, transcriptomics, and spatial biology datasets.
• Hands-on experience with ML/AI methods for clustering, classification, dimensionality reduction, biomarker discovery, and predictive modeling.
• Strong communication skills, with the ability to present complex data to both scientific and non-scientific audiences.
• A proactive and collaborative approach to problem-solving in a dynamic, fast-paced research environment.
• Ability to manage multiple projects simultaneously and adapt to changing priorities.
• Experience contributing to scientific publications, conference presentations, and regulatory submissions, as applicable.
• Key Competencies:
• Demonstrated ability to work independently while contributing effectively to multidisciplinary teams.
• Strong organizational skills with the capacity to manage competing priorities and deliver high-quality work within deadlines.
• Commitment to continuous learning, innovation, and staying current with the latest developments in proteomics and computational biology.
• Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:

Benefits

Additional Information

The Immune Profiling and Systems Immunology (IPSI) team in Discovery Immunology at Cambridge, Massachusetts is seeking a highly skilled candidate (senior scientist) with expertise in spatial proteomics, single cell multi-omics data integration, and machine learning/artificial intelligence. The ideal candidate will have a strong background in bioinformatics and computational biology, with deep experience analyzing complex proteomics and spatial datasets, including CyTOF, Flow Cytometry and Imaging Mass Cytometry (IMC) - based proteomics, and related high-dimensional data modalities. This role will support the analysis of internal datasets and contribute to the identification of novel therapeutic targets for autoimmune and chronic inflammatory diseases.

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