Bioinformatics Scientist-Computational Metabolomics & Exposomics

The Biomarkers Core Laboratory at the Irving Institute for Clinical and Translational Research seeks a Bioinformatics Scientist with expertise in high-resolution mass spectrometry datasets and untargeted HRAM metabolomics data processing. This position supports precision medicine metabolomics and exposomics initiatives in close collaboration with the Center for Innovative Exposomics at the Mailman School of Public Health. The successful candidate will play a central role in the development, optimization, and implementation of computational workflows for large-scale multi-omics integration of metabolomics and exposomics data to advance our understanding of environmental drivers of human health and disease.

Following an initial onboarding period, this position will be eligible for hybrid work.

Bachelor's degree in computational biology, bioinformatics, analytical chemistry, biochemistry, or a related field. Master's degree preferred. 3-4 years of professional experience in bioinformatics, computational metabolomics, or high-resolution mass spectrometry data analysis.

• Independently execute and optimize computational pipelines for spectral processing, feature detection, annotation, statistical modeling, and data visualization across large-scale LC-HRMS datasets.
• Independently process, analyze, and interpret large-scale untargeted metabolomics datasets and deliver structured analytical outputs to investigators.
• Provide oversight of QC monitoring, longitudinal dataset evaluation, and analytical reproducibility across metabolomics and exposomics projects.
• Develop and implement automated workflows integrating R, Python, and related programming environments to support high-volume metabolomics and exposomics data processing.
• Develop and apply bioinformatic tools and computational strategies to address complex analytical challenges and support multi-omics integration.
• Contribute to manuscript preparation and collaborative scientific publications and present findings in internal meetings and collaborative settings.
• Perform data management and archiving according to best practices to ensure reproducibility and regulatory compliance and prepare SOPs, reports, and presentations.

• Demonstrated experience working with LC-HRMS datasets, including feature-level data interpretation and development or curation of spectral libraries.
• Expertise in large-scale untargeted metabolomics data processing, including spectral preprocessing, longitudinal QC monitoring, feature extraction, and statistical analysis.
• Experience with metabolomics data analysis platforms such as MZmine, MS-DIAL, XCMS, Compound Discoverer, Asari, Metabo Analyst or similar tools.
• Proficiency in one or more programming languages (R, Python, Java, or equivalent). Experience working in high-performance computing environments such as AWS is preferred.
• Demonstrated experience applying multivariate statistics and data mining approaches, including PCA, hierarchical clustering, ANOVA, SVM, PLS, pathway analysis, and network analysis.
• Strong organizational skills and the ability to manage multiple concurrent projects.
• Effective communication skills with the ability to translate computational findings for multidisciplinary research teams.

Note: Compensation is commensurate with experience. Interested candidates should send a resume to rn2374umbia.edu.