Computational Biologist - Spatial Multi-Omics
- Build and maintain scalable pipelines for spatial and deep visual multi-omics analysis, including data ingestion, QC, normalization, batch correction, feature extraction, and annotation from mass-spectrometry and transcriptomics platforms.
- Integrate spatial metabolomics/proteomics with transcriptomics, genomics, and histopathology images to deliver multi-modal insights for target discovery, biomarker development, and mechanism-of-action studies.
- Evaluate, benchmark, and optimize tools and workflows; contribute to internal software (R/Python) and visualization frameworks to streamline spatial omics analytics.
- Perform spatially aware statistical analyses to identify regulated molecular markers across tissue regions, cell types, and phenotypes
- Develop and apply algorithms for spatial segmentation, clustering, co-localization, neighborhood analysis, and spatial correlation; conduct pathway/network analyses.
- Collaborate with experimental biologists, pathologists, chemists, and clinicians to shape hypotheses, design studies, and translate findings into decisions for research programs.
- Document pipelines and analyses to ensure reproducibility, compliance, and knowledge transfer; prepare clear visualizations and narratives for internal reviews, publications, and external collaborations.
- Partner with data engineering/IT to manage large spatial datasets, define metadata standards, and implement versioning, governance, and access control best practices.
- PhD in Computational Biology, Bioinformatics, Systems Biology, Biostatistics, Computer Science, or related field; or MSc with substantial relevant experience.
- Hands-on experience analyzing mass spectrometry and transcriptomics spatial data, including QC, normalization, feature extraction, and statistical interpretation.
- Background in image analysis and spatial statistics (segmentation, registration, spatial point patterns, neighborhood analysis). Exposure to machine learning or deep learning for omics or imaging data.
- Familiarity with MS and spatial tools like MZmine, MaxQuant, Proteome Discoverer, Skyline, OpenMS, etc.; and spatial frameworks like Squidpy, Giotto, Seurat/Spatial, Napari, ImageJ/Fiji, CellProfiler, etc.
- Experience with pathway/network analysis (e.g., KEGG, Reactome, MetaboAnalyst, Cytoscape).
- Proficiency in Python and/or R; comfort with Linux/Unix environments, high-performance computing, and version control (Git).
- Demonstrated ability in high-dimensional data analysis, statistics, and reproducible pipeline development.
- Solid understanding of molecular biology, biochemistry, and metabolism to interpret results and design analyses.
- Strong communication skills; experience collaborating within interdisciplinary teams and presenting complex results to diverse audiences
YOUR APPLICATION
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.
Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders. Bayer is an E-Verify Employer. Location: United States : Massachusetts : Cambridge Division: Pharmaceuticals Reference Code: 868556 Contact Us Email: [email protected]
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