Bioinformatics, Analyst V - CGR

Company: Frederick National Laboratory for Cancer Research
Location: Rockville
Posted on: June 26, 2025

Job Description:

Bioinformatics, Analyst V - CGR Job ID: req4334 Employee Type: exempt full-time Division: Clinical Research Program Facility: Rockville: 9615 MedCtrDr Location: 9615 Medical Center Drive, 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 DESCRIPTION We are seeking an experienced senior bioinformatics professional 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), methylation, targeted, whole-exome, whole-transcriptome 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, gene and isoform expression, base modifications, viral and bacterial genomics, and more. Additionally, we advance cancer research by integrating latest technologies such as single cell, multiomics, 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 1000 genomes to inform and validate GWAS signals, study the association between genetic variation and gene expression, protein levels, metabolites and develop polygenic risk scores across multiple populations. Our bioinformatics team develops and implements sophisticated, cloud-enabled pipelines and custom data analysis methodologies, blending traditional bioinformatics and statistical approaches with cutting-edge techniques like machine learning, deep learning, and generative AI models. We prioritize reproducibility through the use of containerization, workflow 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 provide dedicated analytical support to CGR and contribute to cancer research in areas such as germline and somatic variant analysis consisting of SNVs and indels, structural variant, copy number variations and microsatellite detection, followed by variant annotation, filtering and association testing in familial and non-familial datasets. The Bioinformatics Analyst V will also develop and maintain software for the analysis of Human Papillomavirus (HPV) typing assays. They will create new, state-of-the-art, accelerated pipelines and leverage strong data analysis and visualization skills to support downstream analytics, enabling interpretation and the derivation of meaningful biological insights. The analyst will integrate publicly available bioinformatics tools, genomic databases, and large biobanks with internal datasets, enabling independent validation of results across diverse tissues and cancer types. They will work closely with DCEG investigators and CGR scientists, operating with a high degree of independence and demonstrating leadership in project execution. This role involves handling large-scale sequencing data, developing robust pipelines, performing downstream analytical modeling, and collaborating with interdisciplinary teams. KEY ROLES/RESPONSIBILITIES Develop, implement, benchmark and optimize analytical pipelines for germline and somatic variant analysis from short- and long-read whole-genome sequencing (WGS). Ability to interpret variant calling results, encompassing SNP/indel, microsatellite, copy number and structural variant analysis. Apply statistical approaches to annotate, filter and interpret variants in diverse genetic and genomic datasets and integrate findings with clinical and multi-omics data. Maintain and develop assays for HPV typing using Ion Torrent platform. Review and QC genomic datasets, perform downstream statistical analysis using phenotypic and clinical metadata. Demonstrate strong teamwork and communication skills, with the ability to effectively learn and apply new bioinformatics techniques and resources. Maintain and document bioinformatics software and scripts to ensure reproducibility and scalability. Participate in group meetings, present findings, and contribute to publications resulting from research projects. BASIC QUALIFICATIONS To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below: Possession of a Master’s degree from an accredited college or university according to the Council for Higher Education Accreditation (CHEA) in bioinformatics, computer science, computational biology or related field. Foreign degrees must be evaluated for U.S. equivalency In addition to educational requirements, a minimum of ten (10) years of related analytical and bioinformatics pipeline development experience. Strong experience analyzing ultra-deep genomic datasets from fresh-frozen and FFPE specimens using novel approaches and utilizing accelerated and parallel computing. Well-versed with variant benchmarking using the latest community standards. The ability to construct practical computational pipelines for data parsing, quality control, secondary and tertiary analysis requiring in-depth visualization and statistical analysis for large-scale genetic or genomics datasets. Strong experience analyzing human high-throughput sequencing data (WGS, WES, targeted sequencing) and HPV typing in cancer datasets. Experience in analysis of family-based trio datasets from cancer epidemiological studies to discover small and large de novo variants. Prepare detailed reports and present to the investigators. Strong programming skills in R, Python and Java with experience in RStudio and Jupyter Notebooks. Demonstrable shell scripting skills (e.g., bash, awk, sed). Experience working in a Linux environment (especially a HPC environment or cloud). Strong problem solving and ability to tackle and solve complex problems independently. Ability to obtain and maintain a security clearance. PREFERRED QUALIFICATIONS Candidates with these desired skills will be given preferential consideration: A PhD in bioinformatics, computer science, computational biology or related field. Proficiency with core statistical and bioinformatics methods (linear regression, logistic regression, machine learning) and sophisticated data visualization. Experience in standard genetic association analysis software like PLINK, SAIGE, regenie. Expert in R, Python, Java and Bash scripting, and GitHub for code development. Extensive use of markdown documents for sharing and documenting the work. Knowledge of tools to query and investigate cancer genomics with publicly available data sources (such as dbGaP, TCGA,1000 Genomes, gnomAD) and large Biobanks (AoU and UKB). Experience working in Linux-based environments and using HPC (high-performance computing) clusters. Strong experience in genomic data visualization tools such as IGV. Strong understanding of algorithmic efficiency and working on high performance clusters for supporting large and diverse datasets. Expert in environment/dependency management tools (e.g. pip, venv, conda, renv) and workflow management systems such as Snakemake, Nextflow and WDL. Proficiency in using containerization with Docker/Singularity, JIRA for project management. Proficiency in software and workflow development best practices such as source control, test driven programming and continuous integration/deployment. Strong analytical and problem-solving skills with attention to detail. Strong communication skills, and an ability to work both independently and collaboratively. 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 145,800.00 - 250,625.00 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

Keywords: Frederick National Laboratory for Cancer Research, Towson , Bioinformatics, Analyst V - CGR, Science, Research & Development , Rockville, Maryland