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

About the role

The Frederick National Laboratory is seeking a skilled and motivated Computational Scientist to join the Cancer Genomics Research Laboratory (CGR) at the National Cancer Institute (NCI) Shady Grove campus in Rockville, MD. CGR is part of Leidos Biomedical Research, Inc., and collaborates with the NCI’s Division of Cancer Epidemiology and Genetics (DCEG) on groundbreaking research.

Responsibilities

• Lead end-to-end analyses and discussions of single-cell, multi-omics, 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 testing, 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.
• Perform integration of multi-modal omics datasets to support large-scale oncology research initiatives.
• 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.

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.
• Ability to obtain and maintain a security clearance.