Key Responsibilities:

• Design and implement AI/ML models to derive insights from complex, high-dimensional datasets (e.g., EHRs, genomic data, imaging, and biosensor outputs).

• Collaborate with multidisciplinary teams (clinicians, epidemiologists, data engineers, policy analysts) to define research questions and develop data-driven solutions.

• Apply and tailor machine learning techniques to solve domain-specific problems in:

  • Predictive modeling (e.g., risk stratification for TBI, PTSD, chronic illness)

  • Time-series analysis (e.g., vital sign monitoring, disease progression)

  • Natural language processing (NLP) (e.g., extraction from clinical notes, case reports)

  • Computer vision (e.g., radiology and pathology image classification)

  • Unsupervised learning (e.g., clustering phenotypes or patient cohorts)

  • Causal inference and counterfactual modeling for treatment effect estimation

  • Federated learning and privacy-preserving AI for multi-site collaboration

• Support model validation, bias detection, and ethical AI practices in compliance with health data privacy regulations (HIPAA, 21 CFR Part 11).

• Contribute to the development of dashboards, decision support tools, and research publications.

• Participate in proposal development for research funding from DHA, MTEC, NIH, VA, and other agencies.

Required Qualifications:

• Master’s or Ph.D. in Computer Science, Data Science, Biomedical Informatics, Statistics, or related discipline.

• 3+ years of experience applying AI/ML in healthcare, biomedical research, or public health.

• Proficiency in Python, R, and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost.

• Deep understanding of ML concepts: supervised and unsupervised learning, deep learning, ensemble methods, and model evaluation techniques.

• Experience working with health data: EHR, claims, registries, or clinical trial datasets.

• Familiarity with healthcare interoperability standards and terminologies (e.g., HL7 FHIR, SNOMED CT, LOINC).

Preferred Qualifications:

• Prior experience supporting DoD, VA, HHS, or federally funded R&D programs.

• Hands-on experience with NLP libraries (e.g., spaCy, Hugging Face Transformers) and medical imaging tools (e.g., MONAI, OpenCV).

• Understanding of algorithmic fairness, health equity analysis, and SDOH integration.

• Experience with cloud-based data platforms and ML pipelines (e.g., AWS, Azure, Databricks, Snowflake).

What We Offer:

• Comprehensive benefits package, including health, dental, and vision insurance
• Generous Contribution on Health, Dental and Vision Insurances
• 100% Employer Paid Group Life Insurance, Short Term and Long-Term Disability
• Safe Harbor 401(K) Plan
• Health Saving Account (HSA)
• Healthcare Flexible Saving Account (FSA) and Dependent Care FSA
• Education Reimbursement
• Employee Referral Program