Proteomics Specialist

Overview

We are seeking a Computational Biologist with strong extensive proteomics experience to join our Bioinformatics team at the NIH. The computational biologist will independently support –omics projects, specifically, proteomics projects as well as others as needed, initiated by researchers and clinicians at the National Institute of Allergy and Infectious Diseases (NIAID) in the National Institutes of Health (NIH). This opportunity is a full-time position with Guidehouse and can be remote or on-site at NIH in Rockville, MD.

The candidate will operate in a multi-disciplinary group of scientists who provide support, training, and consultation services to the research community in the areas of bioinformatics and computational biosciences. The successful candidate for this position will be well-versed in proteomic methodologies, including but not limited to traditional to high-throughput technologies, self-directed professional who takes ownership of projects and acts autonomously to set priorities and drive results, highly collaborative, and will provide mentorship and leadership as a proteomics expert.

Experience with designing proteomic projects and conducting data analysis using relevant scientific computing software and tools, open-source libraries, data-intensive workloads, and distributed high-performance computing systems is highly desirable. The successful candidate must have excellent written and verbal communication skills to interact with the research community and find the right solution for their diverse scientific analysis and computing needs.

The successful candidate will work cooperatively with current computational biology specialists to:

• 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-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 literature, technologies, and tools
• Partner with software developers to integrate proteomics software solutions within enterprise platforms

What You Will Need:

• Ph.D. (or other graduate degree with equivalent experience) in computational biology, bioinformatics, proteomics or related life, physical, or computational sciences with at least two publications in high-impact journals demonstrating the use or development of proteomics methods
• FOUR (4) years hands on experience with either Computational Biology or Bioinformatics
• Good understanding of high-throughput proteomic technologies, protein-biology, molecular biology, and proteomics software (e.g., Max Quant, Proteome Discoverer, Skyline, Spectronaut, Frag Pipe, Scaffold Quant, etc.)
• Demonstrated proficiency in the analysis of large-scale proteomics data (LC-MS/MS, label-free and labeled-based quantitative proteomics, TMT, iTRAQ, post-translational modification, Olink, Somascan, etc.), proteomics file types (mzML, MGF, pepXML, IDML, etc.) and experienced with a broad spectrum of relevant open-source software or pipelines (Pyteomics, Alpha Pept Stats , MSstats, proteo
  
  DA, MS-DAP, etc.).
• Experience working with relevant proteomics databases and browsers and their annotations (Uni Prot, PDB, STRING, PRIDE, Human Protein Atlas, and neXtProt)
• Proficiency in the use of UNIX/Linux and its command-line environment, including scripting (Python, R, shell, Perl, etc.) as well as experience with code and documentation repositories such as Git Hub.
• Proficiency in pathway analysis translating protein lists into biological insights using enrichment tools (GO, KEGG, IPA) and protein-protein interaction network analysis
• Experience with a high-performance parallel computing environment (e.g., SLURM, PBS, SGE)
• Familiarity with common methods of statistical analysis (linear mixed models, Bayesian approaches) to identify differentially expressed proteins and biomarkers
• 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…