• Partner daily with ML engineers and AI research teams to translate model capabilities into user-facing analytics features that are reliable, interpretable, and clinically meaningful. • Rapidly prototype features and workflows using AI-assisted development tools to validate ideas, test hypotheses, and de-risk engineering investment before committing to full builds. • Design and own the benchmarking and evaluation strategy for AI-driven analytics—define quality standards, build evaluation frameworks, and ensure our platform meets rigorous accuracy, reliability, and performance thresholds. • Work directly with large-scale healthcare data (claims, EHR) using SQL and Python to inform product decisions, validate analytical outputs, and develop intuition for data quality issues. • Define multi-layered success metrics that connect model-level performance (precision, recall, F1) to product-level outcomes (user adoption, task completion) and business-level impact (research acceleration, customer expansion). • Lead cross-functional execution across engineering, data science, design, and clinical/research stakeholders, ensuring alignment and shipping velocity in a fast-moving environment.