# Computational Biologist - Spatial Multi-Omics

**Company**: Bayer
**Location**: Cambridge
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Salary**: $106,400.00 - $159,600.00
**Category**: Engineering
**Industry**: Healthcare
**Wikidata**: https://www.wikidata.org/wiki/Q152051

**Apply**: https://talent.bayer.com/careers/job/562949977101269?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_cf380891-1a3

## Description

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.

## Skills

### Required
- Computational Biology
- Bioinformatics
- Systems Biology
- Biostatistics
- Computer Science
- Mass Spectrometry
- Transcriptomics
- Spatial Statistics
- Machine Learning
- Deep Learning
- Python
- R
- Linux
- Unix
- High-Performance Computing
- Version Control
- Molecular Biology
- Biochemistry
- Metabolism

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Source: [Apply at talent.bayer.com](https://talent.bayer.com/careers/job/562949977101269?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)