• Jobs
• Opportunities
• Services
• Workshops
• News
• Resources
• Stories

Overview of the Role

Biological mass spectrometry is entering a transformative era defined by AI-enabled analysis, increasing data scale, and proteoform-level resolution. This role offers a rare opportunity to shape the analytical foundations of next-generation mass spectrometry-based multiomics (proteomics, metabolomics, lipidomics) and to define how advanced computation and AI unlock new biological and biomedical insights.The Lead of Computational MS and Innovation will be empowered to build new capabilities, pursue bold ideas, and influence the direction of biological mass spectrometry research at an institutional level.

Reporting to the Director of Systems Mass Spectrometry, the scientist will lead cutting-edge analysis of data generated by a broad portfolio of modern MS methods, including bottom-up, top-down, native, cross-linking and spatial mass spectrometry, as well as multiomics (metabolomics and lipidomics). The successful candidate will also contribute to project design, and technology development while serving as a scientific and technical resource for the Institute’s investigators. The position requires deep technical expertise, collaborative spirit, and outstanding interpersonal skills, with regular interaction across more than 20 independent research programs and a spectrum of technology development facilities. They lead will also help establish standard operating protocols for results reporting and will champion cross-technology collaboration that merges new-generation mass spectrometry methods with biological discovery.

Key Responsibilities

Scientific Leadership and Strategy

• Define and execute a long-term computational proteomics and AI innovation strategy aligned with institutional research priorities. • Serve as the intellectual leader for computational analysis of large-scale proteomics, native and top-down proteomics, PTM analysis, and integrative multi-omics datasets. • Identify emerging technologies, analytical paradigms, and AI methodologies that can transform proteomics data interpretation and biological insight. • Partner with computational scientists in other technology centers and PI laboratories to integrate mass spectrometry data with genomics, transcriptomics, and microscopy datasets. • Drive high-impact publications, presentations, and dissemination of novel computational methods.

AI-Driven Proteomics Innovation

Lead the development and deployment of machine learning and AI approaches for proteomics, including:

• Deep learning for peptide and proteoform identification and scoring • AI-based spectral prediction and library-free analysis • Methods for both DIA and DDA acquisition strategies • Intelligent deconvolution, feature detection, and noise reduction • Automated proteoform annotation and confidence assessment • Explore and implement generative AI, foundation models, and representation-learning approaches for proteomics and multi-omics data. • Drive innovation in scalable, automated, and reproducible analysis pipelines for high-throughput proteomics.

Data Analysis and Infrastructure

• Oversee the design, maintenance, and evolution of computational pipelines for proteomics data processing, quality control, statistical analysis, and visualization. • Guide the integration of proteomics data with genomics, transcriptomics, and metabolomics datasets. • Partner with IT and the Big Data team at Stowers to ensure robust data management, cloud/HPC utilization, and FAIR data practices.

Collaboration and Scientific Partnership

• Work closely with experimental proteomics staff, and biological investigators to ensure computational approaches are tightly coupled to experimental design. • Act as a senior scientific consultant for complex studies requiring custom analysis, novel algorithms, or advanced statistical modeling. • Represent computational proteomics expertise in institutional initiatives, external collaborations, and consortium-based projects.

Required Qualifications

• Masters is minimal, PhD in Chemistry, Biochemistry, Proteomics, Bioanalytical Chemistry, or a related field is strongly preferred (or equivalent experience). • Post-doctoral experience and/or 3 to 5 years of work experience post-graduate is strongly preferred. • Demonstrated hands-on experience with computational analysis of native and/or top-down mass spectrometry and proteform discovery, cross-linking mass spectrometry, and spatial mass spectrometry. • Experience with analysis of metabolomics datasets. • Experience with both DIA and DDA methods • Proficiency in Python, R, or similar languages, and familiarity with machine-learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn). • Working knowledge of software platforms such as Proteome Discoverer, Skyline, Compound Discoverer, Xcalibur, or similar. • Experience working in a shared-resource or collaborative research environment. • Demonstrated ability to lead and manage scientific teams and complex projects. • Strong communication, organizational, and interpersonal skills. • Strong publication record.

Preferred Qualifications

• Experience deploying AI/ML models in production scientific environments. • Familiarity with cloud computing (AWS, GCP, or Azure) and workflow managers (Nextflow, Snakemake). • Track record of open-source software contributions in proteomics or multi-omics.

To Apply

Submit the requested documents to careers@stowers.org or to Administration Department, Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110.

Requested Documents:

• Cover Letter • Current Resume

Apply Now

ZoomBio lists openings from official institute and funder websites. Always verify details in the official notice before applying. ZoomBio never charges candidates to apply.