Research Associate, Data Scientist, Biomedical Science

This position is a member of the Computational Biology Program at OHSU and will work in close collaboration with Dr. Yabing Chen's laboratory on computational studies of atherosclerosis and vascular disease.

The position will analyze and integrate single-cell RNA sequencing (scRNA-seq), spatial transcriptomics/spatial omics, bulk transcriptomic, epigenomic, imaging, and related biomedical datasets to define disease-associated cell states, vascular microenvironments, and molecular pathways involved in atherosclerosis. The employee will develop reproducible computational pipelines in R, Python, and Linux; perform quality control, cell-type/state annotation, differential and pathway analyses, spatial and multi-omics integration, and data visualization;

and apply statistical, machine-learning, and computational biology methods to generate biologically testable hypotheses.

The position will work closely with computational scientists, vascular biologists, and experimental investigators in Dr. Yabing Chen's lab and collaborating groups to translate complex omics data into mechanistic insights, figures, manuscripts, and research proposals.

• Analyze scRNA-seq and spatial transcriptomics/spatial omics datasets from atherosclerosis and vascular-disease studies.
• Perform data quality control, normalization, dimensionality reduction, clustering, cell-type and cell-state annotation, differential expression, pathway analysis, trajectory/state-transition analysis, and spatially resolved characterization of vascular lesions and disease-associated cellular niches.

• Integrate single-cell and spatial data with bulk RNA-seq, ATAC-seq, imaging, phenotypic, and other relevant datasets.
• Apply statistical, machine-learning, network, and multi-omics approaches to identify disease-associated regulatory programs, cell-cell interactions, molecular pathways, and candidate mechanisms relevant to atherosclerosis progression and vascular remodeling.

• Develop, optimize, document, and maintain reproducible analysis workflows using R, Python, Linux, Git, and appropriate cloud/container technologies.
• Build scalable data-processing pipelines, structured data systems, publication-quality visualizations, and reusable computational tools that support efficient analysis of high-dimensional genomic and spatial datasets.

• Work closely with Dr. Yabing Chen's laboratory and collaborating investigators to define computational questions, interpret results in the context of atherosclerosis biology, troubleshoot data and analysis issues, and translate findings into figures, presentations, manuscripts, grant applications, and follow-up experimental hypotheses.
• Communicate analytical methods and results clearly to both computational and experimental team members.

Other duties as assigned.

• Master's Degree in relevant field AND3 years of relevant experience; OR
• Bachelor's Degree in relevant field AND 5 years of relevant experience.

• Computational biology, bioinformatics, genomics, single-cell analysis, spatial omics, or related biomedical data science.
• Ability to work in interdisciplinary cardiovascular, vascular biology, atherosclerosis, or translational biomedical research teams.
• Required experience analyzing single-cell RNA-seq (scRNA-seq) and spatial transcriptomics/spatial omics data in atherosclerosis or closely related vascular/cardiovascular disease research.
• Proficiency in R and/or Python for bioinformatics and high-dimensional data analysis, preferably in Linux/Unix environments.
• Experience with scRNA-seq workflows including quality control, normalization, clustering, cell-type and cell-state annotation, differential expression, pathway analysis, and data visualization.
• Experience with spatial-omics analysis, including integration of molecular measurements with tissue location and characterization of spatial cell states, neighborhoods, or disease-associated niches.
• Next-generation sequencing and/or multi-omics data such as bulk RNA-seq, ATAC-seq, genomic, epigenomic, imaging, or related biomedical datasets.
• Machine learning, deep learning, network analysis, image analysis, or computational modeling for biomedical applications.
• Proven proficiency developing…