BioSpace
Bioinformatics Analyst III - SC and Spatial, CGR
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Bioinformatics Analyst III - SC and Spatial, CGR
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BioSpace Bioinformatics Analyst III - SC and Spatial, CGR
2 days ago Be among the first 25 applicants Join to apply for the
Bioinformatics Analyst III - SC and Spatial, CGR
role at
BioSpace Bioinformatics Analyst III - SC and Spatial, CGR
Job ID: req4363
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.
THIS POSITION IS CONTINGENT ON FUNDING APPROVAL***
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 germline and somatic factors that drive cancer susceptibility and outcomes. We are deeply committed to the study of cancer etiology and support of DCEG’s pioneering research program.
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, 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, 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, onprem and 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 using 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 operate with a high degree of independence, providing dedicated bioinformatics support to CGR and DCEG investigators, with a primary focus on analyzing large-scale single-cell, multiomic and spatial transcriptomics as well as proteomics datasets derived from diverse cancer types and tissues. Leveraging deep expertise in single-cell and spatial omics, the analyst will take on a lead bioinformatician role, guiding investigators and team members through all phases of data analysis. Responsibilities will span initial quality control, data visualization and filtering, batch correction, cell segmentation, clustering, annotation for generation of analysis-ready datasets, followed by advanced statistical modeling tailored to address unique scientific questions.
The ideal candidate will possess a strong foundation in statistical, machine learning, and deep learning approaches for data analysis. They will actively stay abreast of emerging developments in the field and integrate newer approaches in their work. This role requires demonstrated expertise in handling large, complex datasets in population-scale studies, developing statistically rigorous methodologies, and collaborating effectively within multidisciplinary research teams to generate meaningful biological insights.
Key Roles/Responsibilities
Provide expert support for single cell, multiomic, and spatial omics projects by collaborating closely with DCEG investigators, CGR bioinformaticians, and CGR laboratory scientists. Implement state-of-the-art computational approaches to support standardized and reproducible analyses for spatial multi-modal omics. Research and benchmark existing and emerging software tools and integrate multi-modal omics data to advance large-scale oncology research. Stay up to date with advancements in the field through literature review, seminars, and cross-disciplinary collaborations. Perform quality control, batch correction, data integration, cell typing, and downstream analysis of single-cell, multiomic, and spatial transcriptomics datasets, incorporating relevant phenotypic and clinical metadata. Apply statistical, machine learning, and deep learning methods for cell segmentation, annotation, and advanced modeling. Present analytical results through clear, interpretable visualizations and reports to support scientific insights and communication. Maintain detailed documentation of analyses, workflows, and pipelines to ensure adherence to FAIR data principles. Position requires routine use of GitHub, JIRA, markdown for documentation. Contribute to manuscript writing, submission, and revisions, with strong opportunities for co-authorship.
Basic Qualifications
To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:
Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related field from an accredited institution. Foreign degrees must be evaluated for U.S. equivalency. In addition to educational requirements, a minimum of five (5) years of directly related analytical or bioinformatics pipeline development experience. Demonstrated experience with single cell, single-nuclear multiomic, and spatial omics data analysis, including a solid understanding of statistical and analytical methods for biomarker discovery and spatial profiling. Ability to work independently and collaboratively with internal and external investigators. Strong programming skills in R and/or Python using tools like RStudio and Jupyter Notebooks. Hands-on experience with tools such as Cell Ranger, Space Ranger, Stardist, Harmony, RCTD, Seurat, SingleR, Scanpy, Squidpy, and Bioconductor packages. Proficient in creating high-quality visualizations using the ggplot2 library in R and data visualization libraries in Python (e.g., Matplotlib, Seaborn, Plotly). Experience with DevOps tools such as Docker/Singularity, and GitHub for code management and reproducible research. Proficiency in shell scripting (e.g., Bash, AWK, SED). Experience working in Linux-based HPC or cloud environments. Strong communication skills and ability to collaborate across diverse research backgrounds. Ability to obtain and maintain a security clearance.
Preferred Qualifications
Candidates with these desired skills will be given preferential consideration:
Master's or PhD in bioinformatics, computer science, computational biology or related field preferred. Experience in analyzing data generated from diverse single cell and spatial applications leveraging both sequencing and imaging methodologies, including 10X Chromium, Visium and Xenium platforms, Bruker/Nanostring GeoMX and CosMX platforms as well as various other short and long read applications using Illumina, PacBio or ONT data. Strong working knowledge of end-to-end processing and analysis of single-cell RNASeq, single-cell ATAC seq, single-nuclear multiomic, and spatial transcriptomic datasets. Experience in development, validation and application of AI and deep learning models for imaging, spatial and multiomic applications. Ability to employ algorithmic efficiency and parallelization on HPC systems for large dataset processing. Proficiency with dependency/environment management tools (e.g., pip, conda, renv, venv). Additional experience analyzing bulk transcriptomics, proteomics, metabolomics, or cancer genomics data from next-generation sequencing platforms is a plus.
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 Seniority level
Seniority level Mid-Senior level Employment type
Employment type Full-time Job function
Job function Research, Analyst, and Information Technology Industries Internet News Referrals increase your chances of interviewing at BioSpace by 2x Get notified about new Bioinformatics Analyst jobs in
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Join to apply for the
Bioinformatics Analyst III - SC and Spatial, CGR
role at
BioSpace Bioinformatics Analyst III - SC and Spatial, CGR
2 days ago Be among the first 25 applicants Join to apply for the
Bioinformatics Analyst III - SC and Spatial, CGR
role at
BioSpace Bioinformatics Analyst III - SC and Spatial, CGR
Job ID: req4363
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.
THIS POSITION IS CONTINGENT ON FUNDING APPROVAL***
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 germline and somatic factors that drive cancer susceptibility and outcomes. We are deeply committed to the study of cancer etiology and support of DCEG’s pioneering research program.
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, 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, 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, onprem and 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 using 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 operate with a high degree of independence, providing dedicated bioinformatics support to CGR and DCEG investigators, with a primary focus on analyzing large-scale single-cell, multiomic and spatial transcriptomics as well as proteomics datasets derived from diverse cancer types and tissues. Leveraging deep expertise in single-cell and spatial omics, the analyst will take on a lead bioinformatician role, guiding investigators and team members through all phases of data analysis. Responsibilities will span initial quality control, data visualization and filtering, batch correction, cell segmentation, clustering, annotation for generation of analysis-ready datasets, followed by advanced statistical modeling tailored to address unique scientific questions.
The ideal candidate will possess a strong foundation in statistical, machine learning, and deep learning approaches for data analysis. They will actively stay abreast of emerging developments in the field and integrate newer approaches in their work. This role requires demonstrated expertise in handling large, complex datasets in population-scale studies, developing statistically rigorous methodologies, and collaborating effectively within multidisciplinary research teams to generate meaningful biological insights.
Key Roles/Responsibilities
Provide expert support for single cell, multiomic, and spatial omics projects by collaborating closely with DCEG investigators, CGR bioinformaticians, and CGR laboratory scientists. Implement state-of-the-art computational approaches to support standardized and reproducible analyses for spatial multi-modal omics. Research and benchmark existing and emerging software tools and integrate multi-modal omics data to advance large-scale oncology research. Stay up to date with advancements in the field through literature review, seminars, and cross-disciplinary collaborations. Perform quality control, batch correction, data integration, cell typing, and downstream analysis of single-cell, multiomic, and spatial transcriptomics datasets, incorporating relevant phenotypic and clinical metadata. Apply statistical, machine learning, and deep learning methods for cell segmentation, annotation, and advanced modeling. Present analytical results through clear, interpretable visualizations and reports to support scientific insights and communication. Maintain detailed documentation of analyses, workflows, and pipelines to ensure adherence to FAIR data principles. Position requires routine use of GitHub, JIRA, markdown for documentation. Contribute to manuscript writing, submission, and revisions, with strong opportunities for co-authorship.
Basic Qualifications
To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:
Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related field from an accredited institution. Foreign degrees must be evaluated for U.S. equivalency. In addition to educational requirements, a minimum of five (5) years of directly related analytical or bioinformatics pipeline development experience. Demonstrated experience with single cell, single-nuclear multiomic, and spatial omics data analysis, including a solid understanding of statistical and analytical methods for biomarker discovery and spatial profiling. Ability to work independently and collaboratively with internal and external investigators. Strong programming skills in R and/or Python using tools like RStudio and Jupyter Notebooks. Hands-on experience with tools such as Cell Ranger, Space Ranger, Stardist, Harmony, RCTD, Seurat, SingleR, Scanpy, Squidpy, and Bioconductor packages. Proficient in creating high-quality visualizations using the ggplot2 library in R and data visualization libraries in Python (e.g., Matplotlib, Seaborn, Plotly). Experience with DevOps tools such as Docker/Singularity, and GitHub for code management and reproducible research. Proficiency in shell scripting (e.g., Bash, AWK, SED). Experience working in Linux-based HPC or cloud environments. Strong communication skills and ability to collaborate across diverse research backgrounds. Ability to obtain and maintain a security clearance.
Preferred Qualifications
Candidates with these desired skills will be given preferential consideration:
Master's or PhD in bioinformatics, computer science, computational biology or related field preferred. Experience in analyzing data generated from diverse single cell and spatial applications leveraging both sequencing and imaging methodologies, including 10X Chromium, Visium and Xenium platforms, Bruker/Nanostring GeoMX and CosMX platforms as well as various other short and long read applications using Illumina, PacBio or ONT data. Strong working knowledge of end-to-end processing and analysis of single-cell RNASeq, single-cell ATAC seq, single-nuclear multiomic, and spatial transcriptomic datasets. Experience in development, validation and application of AI and deep learning models for imaging, spatial and multiomic applications. Ability to employ algorithmic efficiency and parallelization on HPC systems for large dataset processing. Proficiency with dependency/environment management tools (e.g., pip, conda, renv, venv). Additional experience analyzing bulk transcriptomics, proteomics, metabolomics, or cancer genomics data from next-generation sequencing platforms is a plus.
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 Seniority level
Seniority level Mid-Senior level Employment type
Employment type Full-time Job function
Job function Research, Analyst, and Information Technology Industries Internet News Referrals increase your chances of interviewing at BioSpace by 2x Get notified about new Bioinformatics Analyst jobs in
Rockville, MD . Bioinformatics / Computational Biology Attorney / Patent Agent (DC / GA / NC)
Washington, DC $160,000.00-$260,000.00 1 week ago Rockville, MD $47,000.00-$60,000.00 1 month ago Gaithersburg, MD $80,000.00-$85,000.00 1 week ago Gaithersburg, MD $80,000.00-$85,000.00 2 weeks ago Bioinformatics Scientist II, Brain Tumor Institute
Washington, DC $92,684.80-$154,460.80 2 days ago Bethesda, MD $68,000.00-$90,000.00 1 week ago Bioinformatics Analyst III (Computational Biology)
Silver Spring, MD $64,200.00-$90,000.00 1 week ago Bethesda, MD $98,000.00-$116,000.00 3 weeks ago Bioinformatics Analyst III - SC and Spatial, CGR
Annandale, VA $64,200.00-$85,000.00 2 weeks ago We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr