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

Job

Employee Type: exempt full-time

Division:
Clinical Research Directorate

Facility:
Rockville: 9609 Med Ctr Dr

Location:

9609 Medical Center Dr, Rockville, MD 20850 USA

The Frederick National Laboratory is operated by Leidos Biomedical Research, Inc. The lab addresses some of the most urgent and intractable problems in the biomedical sciences in cancer and AIDS, drug development and first-in-human clinical trials, applications of nanotechnology in medicine, and rapid response to emerging threats of infectious diseases.

Accountability, Compassion, Collaboration, Dedication, Integrity and Versatility; it's the FNL way.

We are seeking a skilled and motivated Computational Scientist to join the Cancer Genomics Research Laboratory (CGR), located at the National Cancer Institute (NCI) Shady Grove campus in Rockville, MD. CGR is operated by Leidos Biomedical Research, Inc., and collaborates with the NCI’s Division of Cancer Epidemiology and Genetics (DCEG) - the world’s leading cancer epidemiology research group. Our scientific team leverages cutting-edge technologies to investigate genetic, epigenetic, transcriptomic, proteomic, and molecular factors that drive cancer susceptibility and outcomes.

We are deeply committed to the mission of discovering the causes of cancer and advancing new prevention strategies through our contributions to DCEG’s pioneering research.

Our team of CGR bioinformaticians supports DCEG’s multidisciplinary family- and population-based studies by working closely with epidemiologists, biostatisticians, and basic research scientists in DCEG’s intramural research program. We provide end-to-end bioinformatics support for genome-wide association studies (GWAS) using SNP arrays, methylation arrays, targeted sequencing, whole-exome sequencing, whole-transcriptome sequencing, and whole-genome sequencing, along with viral and metagenomic studies from both short- and long-read sequencing platforms.

This includes the analysis of germline and somatic variants, structural variations, copy number variations, microsatellite analysis, gene and isoform expression, base modifications, viral and bacterial genomics, and more. Additionally, we advance cancer research by integrating the latest technologies, such as single-cell, multi-omics, spatial transcriptomics, and proteomics, in collaboration with the Functional and Molecular and Digital Pathology Laboratory groups within CGR.

We extensively analyze large population databases such as All of Us, UK Biobank, gnomAD, and the 1000 Genomes Project to inform and validate GWAS signals, study associations between genetic variation and gene expression, protein levels, and metabolites, and develop polygenic risk scores across multiple populations.

The bioinformatics team develops and implements sophisticated HPC- and cloud-enabled pipelines and data analysis methodologies, blending traditional bioinformatics and statistical approaches with cutting-edge techniques such as machine learning, deep learning, and generative AI models. We prioritize reproducibility through the use of containerization, workflow and code management tools, thorough benchmarking, and detailed workflow documentation. Our infrastructure and data management team works closely with researchers and bioinformaticians to maintain and optimize a high-performance computing (HPC) cluster, provision cloud environments, and curate and share large datasets.

The successful candidate will demonstrate scientific and technical leadership in analyzing large-scale single-cell, multi-omics, spatial transcriptomics, and proteomics datasets across diverse cancer types, supported by a strong publication record and code repositories that reflect advanced expertise in single-cell and spatial omics data analysis and interpretation. The computational scientist will develop and test hypotheses, design analytical plans, execute end-to-end analyses, and summarize, interpret, and present results while collaborating closely with investigators and scientists.

The candidate will utilize strong knowledge of experimental design, upstream quality control (QC) metric interpretation and visualization,…