Genomics Analyst

RTI International•Durham, NC

1d•$83,000 - $101,000

About The Position

RTI International’s Center of GenOmics and Applied Public Health is hiring an experienced Genomics Analyst to process, quality-check, and analyze genomics, metabolomics, proteomics, and related data generated by collaborative research networks. The analyst will work with investigators, laboratory partners, and multidisciplinary project teams to interpret complex datasets and contribute to research on human health and disease. The position is available in Research Triangle Park, North Carolina, or remotely. The role focuses on platform-appropriate data-processing workflows, including cleaning, filtering, normalization, missing-data assessment, batch-effect evaluation, and, as appropriate, converting raw instrument files into quantified metabolite or protein data. The analyst will develop and document reproducible workflows, prepare analysis-ready datasets and quality-control materials, conduct exploratory and statistical analyses, support integration across omics and other data types, and communicate findings through reports, figures, and research publications. The analyst may also support broader genomic analyses and proposal development.

Requirements

Master’s degree in Bioinformatics, Computational Biology, Biochemistry, Genetics, Statistics, or a related discipline, with at least 1 year of relevant work experience.
Experience managing large biomedical datasets, validating sample and feature metadata, and analyzing high-dimensional data.
Hands-on experience processing metabolomics, proteomics, and/or other omics data, including raw instrument data, with understanding of platform-specific quality control, preprocessing, and preparation of analysis-ready datasets.
Working knowledge of bioinformatics tools and statistical methods for omics data processing, analysis, visualization, and biological interpretation.
Foundational knowledge of human genetics and genomics, including genetic variation, genome builds and annotation, linkage disequilibrium, population structure and genetic ancestry, and common genomic data formats such as VCF, PLINK, and FASTQ/BAM.
Proficiency in R and/or Python, including writing clear, reusable, well-documented code.
Experience working in a Unix/Linux environment.
Strong oral and written communication skills.
Ability to collaborate respectfully and effectively with colleagues across disciplines and research teams.
Ability to manage multiple tasks simultaneously.
Must be legally authorized to work in the United States and must not require now or in the future sponsorship for employment visa status.

Nice To Haves

Proficiency working in high-performance computing and/or cloud computing environments, particularly AWS or Microsoft Azure.
Experience with commercial proteomics platforms such as Olink, including interpreting vendor-provided quality-control metrics and processing delivered data.
Familiarity with mass spectrometry-based metabolomics or proteomics workflows and upstream laboratory processes; wet-lab experience is helpful but not required.
Experience with both metabolomics and proteomics, cross-study harmonization, multi-omics integration, or pathway and enrichment analyses.
Experience with genetic analyses such as genotype quality control and imputation, GWAS, QTL mapping (e.g., pQTL or mQTL), DNA methylation array processing, or RNA-seq analysis, using tools such as PLINK, SAIGE, or Bioconductor packages.
Experience using version control, such as Git, and workflow-management tools to support reproducible analyses.
Experience with human subjects research data.

Responsibilities

Process and quality-check metabolomics, proteomics, and other platform-generated data, including vendor or laboratory data tables and raw instrument files.
Apply platform-appropriate workflows for data cleaning, filtering, normalization, missing-data assessment, and batch-effect evaluation.
Implement and troubleshoot workflows to convert raw instrument data into quantified metabolite or protein data, including feature detection, identification or annotation, and quantification as appropriate.
Develop, maintain, and document reproducible processing and analysis workflows; produce analysis-ready datasets, quality-control summaries, and data dictionaries.
Coordinate with laboratory and data-management partners to receive and validate data deliveries, reconcile sample identifiers and metadata, harmonize datasets across batches or studies, and resolve data-quality issues.
Conduct exploratory and statistical analyses under the guidance of project investigators.
Support integration of metabolomics and proteomics data with other omics, clinical, phenotype, and environmental data.
Support broader genomic analyses as needed, including genotype data quality control and imputation, genome-wide association studies, and integration of genetic data with metabolomic and proteomic data.
Prepare tables, figures, methods descriptions, and technical reports; contribute to manuscripts and communicate processing decisions and analytical results to multidisciplinary teams.
Support proposal development by contributing technical input on data-processing workflows, analytical approaches, and resource needs.