Sr. Clinical AI Informaticist (Computational Epidemiology focus)

Sr. Clinical AI Informaticist (Computational Epidemiology focus)VA Boston Healthcare SystemBoston, MA 02130The 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:

• Epidemiological AI Design & Modeling: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.
• Multi-Omics & EHR Data Integration: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.
• Predictive AI Model Building: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 checkDEGREE: 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 Purushotham.Karnam@va.gov by November 30, 2026. Include “Clinical AI Informatics Scientist” in the e-mail subject line. Please confirm US citizenship in your cover letter. EEO/Equal Opportunity Employer