About the role
Job Family: Scientific Research & Analysis Travel Required: Up to 10% Clearance Required: Ability to Obtain Public Trust We are seeking a Computational Biologist candidate with extensive experience in metagenomics to join our Bioinformatics team at the NIH. The computational biologist will independently support –omics projects, specifically metagenomics 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 work within a multidisciplinary team of scientists who provide support, training, and consultation to the research community in bioinformatics and computational biosciences. The successful candidate will have strong expertise in metagenomic methodologies, bioinformatics tools, and biostatistical approaches, including 16S rRNA gene sequencing, shotgun metagenomics, and long-read metagenomics. This individual will serve as a subject matter expert in metagenomics, providing technical leadership, mentorship, and guidance to colleagues and collaborators. The candidate should be a highly collaborative, self-directed professional who can take ownership of projects, set priorities independently, and drive work to completion. Experience designing metagenomic studies and analyzing data using relevant scientific computing software, open-source tools and libraries, data-intensive workflows, and distributed high-performance computing systems is highly desirable. The successful candidate must also have excellent written and verbal communication skills and be able to engage effectively with the research community to understand diverse scientific analysis and computing needs and identify appropriate solutions. What You Will Do: The successful candidate will work cooperatively with the current computational biology specialists to: Implement, design, develop, and innovate current and emerging computational biology and bioinformatics algorithms aimed to process, analyze, manage, interpret and visualize original scientific data Enter into scientific collaborations with physicians and scientists that include the potential for authorships and acknowledgements in publications Must be able to obtain and maintain a Federal or DoD “public trust”; candidates must receive approved adjudication prior to onboarding with Guidehouse. Candidates with an active public trust or suitability are preferred. Gather detailed information from stakeholders and identify existing tools or develop novel algorithms/tools for performing custom and novel analyses Develop, maintain, document, and deliver training materials and sessions that support collaborators and researchers in applying metagenomics methods and high-throughput data processing workflows. Research, design, and deliver educational materials that promote broader adoption and effective use of computational biology techniques, tools, and software among NIH researchers. Aid collaborators in the design of new study projects, providing advice, and guidance for sequencing methods and analytical or statistical considerations for meeting project goals Provide researchers and collaborators with on-demand support and troubleshooting in the use of computational biology software and pipelines related to metagenomics and high-throughput sequencing Stay current on computational biology literature, emerging technologies, methods, and tools. Partner with software developers to develop and integrate metagenomics software solutions within enterprise platforms What You Will Need: Masters or Ph.D. in computational biology, microbiology, statistics or related life, physical, or computational sciences with at least TWO (2) publications demonstrating the use or development of metagenomic methods Good understanding of high-throughput metagenomic technologies and techniques, bioinformatics, microbial ecology, molecular biology, and metagenomics software (e.g., QIIME2, MetaPhlan, MEGAN, Kraken, Ganon, HUMAnN, etc.) Minimum of TWO (2) years experience in the analysis of large-scale metagenomic data (shotgun metagenomics, amplicon sequencing), metagenomics file types (FASTQ, SAM/BAM, biom, HDF5, etc.) and experienced with a broad spectrum of relevant open-source software or pipelines (DADA2, USEARCH, DIAMOND, Bowtie2, BioBakery, genomic assemblers, CheckM, etc.) Experience working with relevant metagenomic databases and browsers and their annotations (SILVA, RDP, Greengenes, NCBI/RefSeq, IMG/M, GTDB, UHGG, etc) Proficiency in the use of UNIX/Linux and its command-line environment, including scripting (Python, R, Bash, etc.) as well as experience with code repositories such as GitHub or Bitbucket Proficiency in functional and taxonomic annotation of metagenomic data using enrichment and annotation tools (KEGG, eggNOG, InterProScan, Pfam, MetaCyc) Experience with a high-performance parallel computing environment (e.g., SLURM, PBS, UGE) Familiarity with community analyses tools (e.g. phyloseq etc), visualization tools (e.g. ggplots) as well as common methods in multivariate statistical analyses (linear mixed models, Bayesian approaches, differential abundance) and related tools (e.g. MaASLin2). 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 and evaluate new software for metagenomics analyses 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 Proficiency with the use of open-source bioinformatics applications employing ontologies, pathways, and/or networks, at both the individual organism and metagenomic community scales Familiarity with problems and bottlenecks associated with storage and management of metagenomics-scale data What Would Be