Computational Scientist I, Single-cell/Spatial Cancer Genomics, CGR

Key Roles/Responsibilities

• Lead end-to-end analyses and discussions of single-cell, multiomics, spatial transcriptomics, and proteomics projects through close collaboration with DCEG investigators, CGR wet-lab scientists, MDPL scientists, and bioinformaticians.
• Demonstrate strong knowledge in experimental design, hypothesis formulation and sing, and development of analytical aims, leveraging expertise in cancer biology and spatial omics.
• Evaluate existing spatial infrastructure and analytical capabilities and build upon them by implementing state-of-the-art methods for single-cell RNA-seq, single-cell ATAC-seq, multi-omics, and spatial omics analyses.
• Use strong knowledge of community standards and best practices to benchmark existing and emerging software tools and incorporate them into workflows.
• Perform nuclear segmentation and expansion of high-resolution images by comparing various tools and optimize for different tissue and cancer types.
• Evaluate QC metrics and appropriate filtering criteria for downstream processing.
• Perform batch correction, data integration, cell clustering, and annotation.
• Identify and create new single-cell references across different cancer and tissue types.
• Study tumor microenvironments and immune cell infiltration.
• Apply statistical, machine learning, and deep learning approaches required for both upstream and downstream analyses.
• Develop clear, interpretable visualizations and analytical reports to communicate findings and support scientific discovery.
• Conduct reproducible scientific research through documentation of software versions, processes, and pipelines, along with the use of tools such as Markdown documents, Conda and R environments, Docker, Singularity, GitHub, and Snakemake/Nextflow.
• Use High-Performance Compute Clusters and the Slurm scheduler, optimize computational resources and data storage requirements to ensure scalability for large datasets.
• Summarize and interpret findings through clear visualizations and reports, and present results to senior leadership and scientists from diverse backgrounds.
• Contribute to manuscript preparation, submission, and revision processes, with strong opportunities for scientific co-authorship.
• Stay current with advances in the field through scientific literature review, seminars, workshops, and cross-disciplinary collaborations.

Basic Qualifications

• Possession of a PhD degree from an accredited college or university according to the Council for Higher Education Accreditation (CHEA) in Bioinformatics, Computational Biology, Biostatistics, or a related field. Foreign degrees must be evaluated for U.S. equivalency.
• Demonstrated experience with single-cell, single-nucleus multiomic, and spatial omics data analysis, including a solid understanding of statistical and analytical methods for biomarker discovery and spatial profiling.
• Experience working with both sequencing- and imaging-based spatial omics platforms.
• Strong publication record demonstrating the ability to analyze and interpret single-cell and spatial omics datasets.
• Strong programming skills in R and Python, with the ability to work in RStudio, VS Code, and Jupyter Notebooks.
• Strong knowledge of reproducibility practices and version control using Docker, Singularity, GitHub, workflow management systems (Snakemake/Nextflow), R and Conda environments, and Markdown documents.
• Proficiency in shell scripting (e.g., Bash, AWK, SED).
• Proficiency working in Linux-based HPC or cloud environments, with a strong understanding of Slurm and the ability to work with large datasets using best practices for algorithmic efficiency, parallelization, and scalability.
• Ability to work independently and collaboratively with internal and external investigators.
• Strong written, verbal, and presentation skills. Ability to work effectively in a multidisciplinary research environment and communicate technical findings clearly to non-specialist audiences through reports and presentations detailing methodologies and results.
• Efficient and organized data management for large projects.
• Strong work ethic and a proactive, solution-oriented mindset.
• Self-motivated, research-focused professional with a passion for advancing cancer genomics.
• Able to obtain and maintain a security clearance.

Preferred Qualifications

• Minimum of three years of postdoctoral or equivalent experience in academia or industry.
• Experience analyzing data from major single-cell and spatial platforms, including 10x Visium HD, Xenium, and Ultivue.
• Strong working knowledge of end-to-end processing and analysis of single-cell RNA-seq, single-cell ATAC-seq, single-nucleus multiomics, and spatial transcriptomics datasets.
• Proficiency in Cell Ranger, Space Ranger, StarDist, QuPath, and familiarity with HALO for evaluating tissue and data quality, nuclear segmentation, and image analysis.
• Familiarity with nuclear segmentation approaches such as CellPose, Baysor and Proseg, and cell-free spatial segmentation approaches such as FICTURE.
• Proficiency in Seurat, Bioconductor, SpatialData, Scanpy, and Squidpy frameworks for end-to-end analysis.
• Strong knowledge of:
• Harmony, FastMNN, and RPCA for batch correction
• Non-spatial (Leiden, Louvain) and spatial (Banksy, spaGCN) clustering algorithms
• RCTD, Azimuth, and SingleR for label transfer and cell annotation
• Cell-cell communication tools such as LIANA, CellChat, and CellPhoneDB
• DESeq2 for differential expression analysis
• Niche and pathway enrichment analysis (GSEA)
• Visualization tools such as Loupe Browser, IGV, and UCSC Genome Browser
• Major file formats such as BAM and Parquet
• Proficiency in creating high-quality visualizations using ggplot2 in R and data visualization libraries in Python (e.g., Matplotlib, Seaborn, Plotly).
• A public code portfolio (e.g., GitHub, GitLab) demonstrating relevant expertise.
• Additional experience analyzing bulk transcriptomics, proteomics, metabolomics, or cancer genomics data from next-generation sequencing platforms is a plus.