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

Job ID: req4609
Employee Type: exempt full-time
Division: Clinical Research Directorate
Facility: Rockville: 9609 MedCtrDr
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.

PROGRAM DESCRIPTIONOur 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, nuclear and cell segmentation approaches, and post-segmentation sample-level QC, filtering, and clustering to generate high-quality results from large projects. Additional expertise should include multi-sample data integration, batch correction, QC, coarse- and fine-grained clustering, label transfer, and cell-type annotation. The candidate should also possess expertise in advanced downstream statistical modeling tailored to address important scientific questions, with a strong foundation in statistical, machine learning, and deep learning approaches for biological data analysis.In addition, the candidate must possess strong scientific literature review and research skills to stay current with emerging developments in the field and incorporate new analytical approaches into their work. This role requires demonstrated expertise in handling large and complex datasets and collaborating effectively within multidisciplinary research teams to generate meaningful biological insights.KEY ROLES/RESPONSIBILITIESBASIC QUALIFICATIONSTo be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:PREFERRED QUALIFICATIONSCandidates with these desired skills will be given preferential consideration:

Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, color, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

Pay and Benefits

Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available here

103,100.00 - 147,767.00 USD

The posted pay range for this job is a general guideline and not a guarantee of compensation or salary. Additional factors considered in extending an offer include, but are not limited to, responsibilities of the job, education, experience, knowledge, skills, and abilities as well as internal equity, and alignment with market data.

The salary range posted is a full-time equivalent salary and will vary depending on scheduled hours for part time positions