• Provide hands-on technical leadership for the three required AI/ML models using CASCOM, GCSS-Army, SAP, and other Army data. • This person must be capable of coding, evaluating, deploying, and sustaining models—not merely leading strategy discussions. • The potential use cases include GenAI/OpenAI, Copilot, SAP ECC search, document extraction, anomaly detection, equipment-readiness forecasting, maintenance forecasting, fleet automation, and supply forecasting. • Lead selection and refinement of the three baseline AI/ML use cases. • Define the mission question, data requirement, technical baseline, performance metric, and acceptance criteria for each model. • Perform data exploration, feature engineering, model development, comparative evaluation, and error analysis. • Work with the GCSS-Army SME to validate business rules and interpret model results. • Work with the Azure Data/MLOps Engineer to package, deploy, monitor, version, and sustain models. • Develop GenAI, RAG, forecasting, anomaly detection, or recommendation capabilities as selected. • Establish human-review procedures where model outputs affect operational decisions. • Prepare model cards, evaluation results, known limitations, release documentation, and retraining criteria. • Support integration of model findings into dashboards and Power Apps. • Participate in demonstrations and CASCOM production-readiness reviews. • Ability to work remotely during normal Government working hours and participate in daily status, requirements, development, and technical-review sessions. • Ability to travel to Fort Lee, Virginia, at least three times annually and report onsite more frequently if directed.

• Support enterprise cardiovascular quality initiatives, including: MIPS/MVPs, Clinically Integrated Network, and payer-specific performance reporting. • Analyze provider-, practice-, and network-level quality performance data. • Develop and maintain dashboards, scorecards, and routine performance reports. • Identify trends, variation, and opportunities for quality improvement. • Collaborate with physician leaders and quality staff to support quality improvement initiatives. • Support Cardiovascular Associates of America’s clinical research portfolio, including: Registry-based and real-world evidence studies, Outcomes research and health services research, Investigator-initiated and industry-sponsored studies. • Assist with cohort development, data extraction, and statistical analyses. • Support study feasibility assessments and analytic plans under senior guidance. • Contribute to abstracts, manuscripts, posters, and internal research reports. • Support dissemination of research findings to inform clinical practice and strategy.

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise. • Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations. • Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions. • Implement new approaches for working with “big data” in healthcare.

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise. • Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations. • Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions. • Implement new approaches for working with “big data” in healthcare. • Some tasks may include data visualization, predictive analytics and machine learning algorithms.