Position Summary
The Biomarkers Core Laboratory seeks a bioinformatics scientist (Staff Associate II) with expertise in high-resolution mass spectrometry datasets and untargeted HRAM metabolomics data processing. This position supports the precision-medicine metabolomics and exposomics initiatives at the Irving Institute for Clinical and Translational Research, 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-omic integration of metabolomics and exposomics to advance our understanding of environmental drivers of human health and disease.
Duties
60%: Independently process, analyze, and interpret large-scale untargeted LC-HRMS metabolomics datasets. Execute and maintain high-performance computational pipelines for spectral processing, feature detection, annotation, statistical modeling, and data visualization. Perform QC monitoring and longitudinal dataset assessment to ensure analytical reproducibility and data integrity across projects. Apply bioinformatic tools and computational strategies to address analytical challenges in metabolomics and multi-omic data integration.
25%: Develop, implement, and optimize automated workflows integrating R, Python, and related programming environments to support high-throughput metabolomics and exposomics data processing. Maintain and support in-house analytical platforms and associated computational infrastructure; recommend and implement incremental improvements as needed.
15%: Prepare structured analytical reports, visualizations, and summary statistics for investigators. Contribute to manuscript preparation and collaborative scientific publications. Maintain SOPs, documentation, and data archiving practices to ensure reproducibility and regulatory compliance.
Functional Knowledge
- Applied knowledge of high-resolution LC-HRMS data structures, untargeted metabolomics workflows, feature-level interpretation, and spectral library curation. Demonstrated understanding of multivariate statistical analysis and data mining approaches relevant to metabolomics datasets.
Problem Solving
- Applies strong analytical skills to troubleshoot data processing challenges, optimize computational workflows, and ensure reproducibility across complex, large-scale metabolomics datasets. Exercises independent judgment when evaluating QC metrics, alignment issues, and feature annotation strategies.
Decision Making/Autonomy (Must equal 10)
- Level of supervision required, on a scale of 1 (least) to 10 (most) - #4
- Degree of independent judgment expected, on a scale of 1 (least) to 10 (most) - #6
Leadership
- Acts as a resource regarding computational metabolomics workflows.
- Contributes to the development and refinement of best practices in data processing, QC monitoring, and reporting.
Technical Expertise
- Advanced experience working with LC-HRMS datasets, spectral preprocessing, feature extraction, and statistical modeling in support of large-scale untargeted metabolomics studies.
Communication Skills
- Effectively translates computational findings into clear, structured reports
- Contributes to peer-reviewed publications and presents findings in collaborative research settings.
Columbia University is an Equal Opportunity Employer / Disability / Veteran
Pay Transparency Disclosure
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.
