• Lead the end-to-end lifecycle of machine learning and statistical models, from problem framing and data acquisition through deployment, monitoring, and retirement. • Design and implement models using causal inference and Bayesian methods to support high-impact operational and strategic decisions. • Develop and maintain model risk management practices, including validation, back-testing, performance thresholds, and issue remediation. • Create and maintain model cards and other documentation that clearly describe model purpose, assumptions, limitations, performance, and governance controls. • Embed ethical AI principles into model design and usage, including fairness, transparency, explainability, and human-in-the-loop oversight. • Architect and scale analytical solutions on DoD data fabrics and cloud environments, collaborating with cloud architects and engineering teams. • Define and track model and analytics performance against AQLs and QASP metrics; proactively recommend improvements to meet or exceed targets. • Partner with security, infrastructure, and operations teams to ensure models comply with cybersecurity, data protection, and configuration management policies. • Provide technical leadership and mentorship to junior data scientists and analysts, reviewing work products and promoting best practices. • Prepare and deliver briefings, dashboards, and written reports that distill complex analytics into clear, actionable insights for senior leaders. • Support proposal and capture efforts by contributing data science subject matter expertise and solution concepts as needed.