Bioinformatics Analyst III, ITEB, CGR
Location: Rockville
Posted on: June 23, 2025
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Job Description:
Bioinformatics Analyst III, ITEB, CGR Job ID: req4285 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 a skilled and motivated 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 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 the
Integrative Tumor Epidemiology Branch (ITEB) and contribute to
cancer research in areas such as GWAS, germline and somatic variant
analysis, single-cell RNA sequencing, and proteomics expression
analysis. The bioinformatics analyst will support the installation,
troubleshooting, and execution of analytical pipelines using
open-source scientific software on Unix/Linux and cloud-based
platforms. They will leverage publicly available bioinformatics and
genomic databases, as well as analysis pipelines, to process
various data types, including genome-wide genotyping arrays,
long-read DNA sequencing, gene expression, proteomic profiling, and
methylation profiling across diverse tissues and cancer types.
Working closely with DCEG investigators and CGR bioinformaticians
and scientists, the analyst will operate with a high degree of
independence. This role involves handling large-scale sequencing
data, developing robust pipelines, and collaborating with
interdisciplinary teams to derive meaningful biological insights.
The candidate will be expected to: KEY ROLES/RESPONSIBILITIES
Develop, implement, and optimize analytical pipelines for germline
and somatic variant analysis from short- and long-read whole-genome
sequencing (WGS). Ability to run and interpret variant calling
results, including SNP/indel, microsatellite, and structural
variant analysis, using the latest community standards. Conduct
association analyses of large GWAS datasets using widely used
software such as PLINK and Genome-wide Complex Trait Analysis
(GCTA). Apply statistical approaches to interpret diverse genetic
and genomic datasets and integrate findings with clinical and
multi-omics data. Collaborate with a multidisciplinary team to
develop and analyze reproducible, standardized workflows for
single-cell and proteomics studies by integrating the latest
research developments with strong programming skills. Review, QC,
and integrate single-cell and proteomic datasets, performing
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
bachelor’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
six (6) years of related analytical or bioinformatics pipeline
development experience. The ability to construct practical
computational pipelines for data parsing, quality control and
analysis for large-scale genetic or genomics datasets. Strong
programming skills in at least two of R, Python, C++, with
experience in RStudio and Jupyter Notebooks. Strong experience
analyzing high-throughput sequencing data including whole-genome,
bulk and single-cell RNA sequencing. Experience in standard genetic
association analysis software like PLINK, SAIGE, regenie, GCTA etc.
Demonstrable shell scripting skills (e.g., bash, awk, sed).
Experience working in a Linux environment (especially a HPC
environment or cloud). Ability to obtain and maintain a security
clearance. PREFERRED QUALIFICATIONS Candidates with these desired
skills will be given preferential consideration: Strong proficiency
in programming (R, Python and Bash) and GitHub. Provide support for
analysis of genomic data from epidemiological studies. This
includes but is not limited to data manipulation, and integrated
genomic. analyses. Prepare various reports and presentations
detailing analyses of data. Proficiency with core statistical and
bioinformatics methods (linear regression, logistic regression,
eQTL analysis, LDscore regression, fine-mapping, credible set and
colocalization analysis, etc.) Experience or familiarity with
processing of single-cell data utilizing latest bioinformatics
tools such as Cell Ranger, Seurat, Scanpy, Squidpy, Cell2location
etc. Familiarity and working knowledge of tools to query and
investigate cancer genomics with publicly available data sources
(such as dbGaP, TCGA, ENCODE, 1000 Genomes, GTEX, gnomAD,
cBioPortal, TCPA). Experience working in Linux-based environments
and using HPC (high-performance computing) clusters. Strong
experience with large-scale multi-omics data integration (e.g.,
genomics, genetics, transcriptomics, proteomics). Good
understanding of algorithmic efficiency and working on high
performance clusters for supporting large and diverse datasets.
Experience with various environment/dependency management tools
(e.g. pip, venv, conda, renv) and workflow management systems such
as Snakemake or Nextflow. Knowledge of containerization with
Docker/Singularity, JIRA and GitHub for project management.
Understanding of 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
the ability to work both independently and collaboratively as part
of team. 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 109,600.00 - 188,250.00 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: , Arlington , Bioinformatics Analyst III, ITEB, CGR, Science, Research & Development , Rockville, Virginia