Computational Scientist

The Stowers Institute for Medical Research; Kansas City (MO); On-site; USD 85,000 - 110,000; Full time

Job summary

The Stowers Institute for Medical Research is seeking a Lead of Computational Mass Spectrometry in Kansas City. This role involves defining long-term strategies for computational proteomics and AI innovation while leading data analysis efforts, shaping the future of biological mass spectrometry research.

Ideal candidates will hold a PhD in a related field with significant experience in mass spectrometry analysis and strong leadership skills. The position offers an opportunity to work within a dynamic, collaborative research environment that pushes the boundaries of scientific discovery.

Qualifications

• PhD in Chemistry, Biochemistry, Proteomics, or a related field.
• 3-5 years of experience in relevant work post-graduation.
• Demonstrated hands-on experience with mass spectrometry analysis.

Responsibilities

• Define and execute computational proteomics and AI strategies.
• Lead the development of machine learning approaches.
• Oversee design and maintenance of computational pipelines.

Skills

Computational analysis

Leadership skills

Machine learning

Python

R

Education

PhD in Chemistry or related field

Post-doctoral experience

Tools

Proteome Discoverer

Skyline

Xcalibur

Job description

The Stowers Institute for Medical Research seeks an accomplished computational scientist to serve as Lead of Computational Mass Spectrometry (MS) and Innovation, within our Systems Mass Spectrometry (SMS) Technology Center. The leadership role sits at the intersection of innovative technology, scientific collaboration, and the Institute’s mission to advance our understanding of life’s fundamental processes. The successful candidate will help drive a cutting‑edge core facility at the heart of a vibrant, multidisciplinary research community, and is expected to bring a strong track record in mass spectrometry data analysis, reporting, and methodological innovation, together with exemplary communication, collaboration, and leadership skills.

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. Their 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.

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
• 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 [e-mail] or to Administration Department, Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110.