We are currently searching for a Computational Genomics Specialist, Clinical to provide support to the National Institutes of Health (NIH). This opportunity is a full-time position with MSC, and it is on-site in Rockville and Bethesda, MD.
Duties & Responsibilities
- Implement, design, develop, and innovate current and emerging computational biology and bioinformatics algorithms to analyze, manage, interpret, visualize, and illustrate original scientific data.
- Enter into scientific collaborations with physicians and scientists that include the potential for authorships and acknowledgements in publications.
- Gather detailed requirements from stakeholders and identify existing tools to perform the novel analyses or develop algorithms/tools to perform the analysis.
- Document and manage collaborative and consultant assistance and training provided to researchers.
- Provide on-site, on-demand support and troubleshooting to researchers in the use of computational biology software.
- Research, design, and deliver training materials to effectively communicate, promote, and advance computational biology techniques and software usage by NIH researchers.
- Remain abreast on current and emerging computational biology technologies and tools.
- Partner with software developers to integrate genomic software solutions within enterprise platforms.
- Ph.D. (or MS with 3+ years of relevant experience, or BS with 5+ years of relevant experience) in computational biology, bioinformatics, genetics or related life, physical, or computational sciences with at least one publication demonstrating the use or development of computational biology applications.
- Good understanding of genomics, molecular biology, and technologies such as qPCR and next-generation sequencing.
- Two years of experience in the analysis of large-scale genomic sequencing data including variant analysis from whole genome or exome sequencing data and experience with a broad spectrum of relevant open-source software or pipelines (e.g., BWA, GATK, SnpEff, VEP, etc.)
- Proficiency in the use of UNIX/Linux and its command-line environment, including scripting (e.g., Python, R, Perl, shell, or Ruby).
- Experience with a high-performance parallel computing environment
- Strong interpersonal, presentation, written, and oral communication skills to convey computational biology principles and concepts to non-specialists in a clear and precise manner and advise on relevant software and tools with a dedication to customer satisfaction.
- Ability to work independently or as part of a multi-disciplinary team.
- Excellent troubleshooting and problem-solving skills, including the ability to learn new software quickly.
- Ability to concurrently work on multiple complex projects with effective time management skills, a high level of personal and professional drive and initiative, and attention to detail.
- Experience with genomic diagnosis of rare diseases and filtering variants using family-based genetic models (e.g., autosomal recessive/dominant, compound heterozygous, etc.), including experience with relevant related tools for finding disease-causing variants (e.g. VAAST/pVAAST, GEMINI).
- Experience with structural variation analysis from next-generation sequencing data
- Experience constructing pipelines in open architecture platforms (e.g. Snakemake), including end-to-end tasks for NGS analysis tools.
- Experience with genetic association methods (case-control, burden testing, family-based linkage analysis).
- Experience with RNA-seq + genome assembly using tools such as HISAT2, STAR, DESeq2.
- Familiarity with common methods of statistical analysis using R, SAS, or related software.
- Familiarity with algorithms and algorithm development for bioinformatics, including composing, editing, and compiling open-source code.
- Familiarity with problems and bottlenecks associated with storage and management of genomic-scale data.
- Familiarity with the use of open-source bioinformatics applications employing ontologies, pathways, and/or networks, at both the individual gene and genomic scales.
- Strong background in molecular/cellular biology, immunology, and/or virology, including “bench” experience.
- Ability to propose and develop new methodologies for analyzing biomedical data.
Dovel Technologies and its Family of Companies (Medical Science & Computing and Ace Info Solutions) was acquired in October 2021.
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