Sr. Clinical AI Informaticist (Computational Epidemiology focus)

VA Boston Healthcare System; Boston (MA); On-site; USD 130,000 - 180,000; Full time

Job summary

VA Boston Healthcare System is seeking a Sr. Clinical AI Informaticist focused on computational epidemiology to advance translational research and precision medicine.

You will bridge clinical informatics, molecular biology, and AI, integrating EHR data with multi-omics to build predictive models and forecast population health trends. The role leads data pipelines, model development, and methodological publications while collaborating with clinicians, researchers, and software engineers at a

Qualifications

• Must be a U.S. citizen and clears a US government background check.
• Degree: Bachelor’s in computer science or BA/BS with 30 semester hours in math/stat/computer science, with calculus.
• Experience: at least one year of related experience at the GS-11 level or equivalent education substitution per OPM.

Responsibilities

• Apply advanced epidemiological designs and causal inference to train predictive AI/ML models for disease incidence and outcomes.
• Architect data pipelines to harmonize multi-omics with EHR data.
• Develop and validate ML/DL models for high-dimensional data on disease susceptibility and treatment response.
• Perform feature selection and dimensionality reduction to identify biomarkers and clinical variables.
• Audit training data to mitigate bias, confounding, and data drift for equitable AI outputs.
• Lead cross-functional collaboration and contribute to scientific publications and documentation.

Skills

Biostatistics

Epidemiology

ML/AI

Leadership

Communication

Problem solving

Education

Bachelor’s degree in computer science

Math/stat coursework with calculus

Tools

Python

R

SQL

PyTorch

Scikit-learn

Job description

Sr. Clinical AI Informaticist (Computational Epidemiology focus)

The role is based within the VA Boston Healthcare System, a leading healthcare system in New England that serves as a major referral center and a hub for clinical research. The role supports innovative research initiatives at the intersection of clinical information, computational epidemiology, and artificial intelligence.

The Principal Clinical AI Informaticist bridges the gap between clinical informatics, molecular biology, machine learning, and population health. We are seeking a senior data scientist with an advanced degree to leverage epidemiological frameworks to integrate clinical phenotypic Electronic Health Records (EHR) data with high-dimensional multi-omics datasets, training and validating highly accurate predictive AI models to identify disease risk, forecast population-level health trends, and optimize precision medicine initiatives.

You will join a multidisciplinary team of clinicians, researchers, data scientists, and software engineers at a nationally recognized Center for Genomics research with access to one of the world’s largest clinical and omics datasets. This role offers a unique opportunity to develop and implement advanced Artificial Intelligence/Machine Learning methods and models for advancing highly impactful translational research and precision medicine.

Primary Duties:

• Apply advanced epidemiological study designs and causal inference methods to build, train, and validate predictive AI/ML models that forecast disease incidence, progression, and treatment outcomes across diverse populations.
• Architect data pipelines that extract, harmonize, and integrate complex multi-omics data (genomics, transcriptomics, proteomics) with structured and unstructured clinical data from Electronic Health Records.
• Develop, train, and validate machine learning and deep learning models optimized for high-dimensional data to predict disease susceptibility, progression, and treatment response.
• Feature Selection & Dimensionality Reduction: Apply advanced statistical techniques to identify key molecular biomarkers and clinical variables, reducing noise in massive multi-dimensional datasets.
• Algorithmic Bias & Confounding Mitigation: Leverage epidemiological expertise to audit AI training datasets, identifying and correcting selection bias, confounding variables, and data drift to ensure equitable, generalizable, and ethically sound clinical AI outputs.
• Leadership, Collaboration & Scientific Communication: Serve as the subject matter expert on clinical data and machine learning methods to guide cross-functional teams of clinicians, biologists, and bioinformaticians and mentor junior scientists and engineers in advanced genomic medicine. The role will also draft and publish scientific publications in peer-reviewed journals, lead other technical documentation, methodology white papers, and patent applications.

Minimum Qualifications:

• MUST be a U.S. citizen and MUST clear a US government background check
• DEGREE: Bachelor’s degree in computer science or bachelor's degree with 30 semester hours in a combination of mathematics, statistics, and computer science. At least 15 of the 30 semester hours must have included any combination of statistics and mathematics that included differential and integral calculus. All academic degrees and coursework must be from accredited or pre-accredited institutions.
• EXPERIENCE: Must have at least one year of related experience at the lower GS level. Per OPM, the minimum qualification requirements are one year of specialized experience equivalent to the next lower grade level in federal service (GS-11 for GS-12 fills) or education substitution for GS-12 or GS-13 research jobs (Ph.D. or equivalent doctoral degree in related field to position being hired into)

Note:

Reference OPM website for more information on Qualification Standards: https://www.opm.gov/policy-data-oversight/classification-qualifications/general-schedule-qualification-standards/1500/computer-science-series-1550/

Preferred qualifications / Core Competencies:

• Experience must demonstrate progressively more complex computer science and data science analytics. Expert-level understanding of biostatistics (survival analysis, logistic regression, AUC-ROC evaluation), rigorous knowledge of clinical workflows, and exceptional communication skills to present molecular and mathematical concepts to non-technical stakeholders.
• Mastery of languages and frameworks used in epidemiological modeling and AI training (Python, R, SQL, PyTorch, Scikit-learn).
• Exceptional ability to navigate sparse and highly high-dimensional datasets while ensuring strict statistical rigor (e.g., multi-test corrections, false discovery rates).
• Strong collaborative mindset with the capability to connect abstract mathematical equations to practical human biological hypotheses.

Note:

This posting is for candidate identification only. Resumes/Curriculum Vitae (CV) will be reviewed, and a shortlist may be interviewed. Any final recommendations for hire will be submitted to Human Resources for qualification review

Point of contact for this position:

Applicants should submit a cover letter, Resume/CV (must indicate full-time or part-time and include hours for each position listed under work experience), and unofficial transcripts to [e-mail] by November 30, 2026. Include “Clinical AI Informatics Scientist” in the e-mail subject line. Please confirm US citizenship in your cover letter.