Spatial Multi-Omics Scientist — Target Discovery & Biomarkers

Biopharma Careers; Cambridge (MA); On-site; USD 106,000 - 160,000; Full time

Job summary

Bayer is seeking a Computational Biologist – Spatial Multi-Omics to join the Translational Sciences Cardiovascular Renal Team at the Kendall Sq, Cambridge, MA campus. You will build scalable pipelines for spatial multi-omics analyses and integrate diverse data types to enable target discovery and biomarker development.

The role emphasizes development of novel analysis methods, collaboration with scientists and clinicians, and ensuring reproducible, well-documented workflows suitable for internal

Qualifications

• 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.

Skills

Mass spectrometry

Transcriptomics spatial data

Image analysis

Spatial statistics

Python

R

Git

High-performance computing

Education

PhD in Computational Biology

MSc with relevant experience

Tools

MZmine

MaxQuant

Proteome Discoverer

Skyline

OpenMS

Squidpy

Giotto

Seurat/Spatial

Napari

ImageJ/Fiji

CellProfiler

Job description

Bayer is seeking a Computational Biologist – Spatial Multi-Omics to join the Translational Sciences Cardiovascular Renal Team at the Kendall Sq, Cambridge, MA campus. You will build scalable pipelines for spatial multi-omics analyses and integrate diverse data types to enable target discovery and biomarker development.

The role emphasizes development of novel analysis methods, collaboration with scientists and clinicians, and ensuring reproducible, well-documented workflows suitable for internal