Computational Biologist - Spatial Multi-Omics

Bayer•Cambridge, MA

16h•Onsite

About The Position

Bayer has an opening for a 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. We are seeking a highly skilled Bioinformatics Scientist to join our team, focusing on the development and maintenance of scalable pipelines for spatial and deep visual multi-omics analysis. 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.

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

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.