Computational Biologist - Spatial Multi-Omics

At Bayer, we're seeking a highly skilled Computational Biologist – Spatial Multi-Omics to join our Translational Sciences Cardiovascular Renal Team based at the Bayer Innovation Campus in the heart of Kendall Sq, Cambridge, MA.

The successful candidate will play a crucial role in integrating various omics data types to drive insights for target discovery, biomarker development, and mechanism-of-action studies.

Key Responsibilities:

• 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.

Requirements:

• 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.