• Own end-to-end execution of ML programs across data collection, labeling, training, evaluation, and on-device deployment—defining scope, milestones, risks, and success metrics tied to accuracy, latency, and in-store experience. • Build and maintain cross-functional plans with computer vision/ML, Android, hardware, MLOps, data engineering, QA, and field operations; run cadences, surface tradeoffs, and drive on-time, high-quality releases. • Lead data operations at scale, including in-store capture, synthetic generation, and third-party annotation—setting data quality bars, SLAs, and privacy standards in partnership with Legal and Security. • Establish rigorous evaluation frameworks that connect offline metrics and on-device performance to business outcomes (e.g., recognition accuracy, shrink reduction, throughput), including pilots and A/B tests in stores. • Drive deployment readiness and reliability: model packaging for edge devices, rollout plans, monitoring and alerting, incident response, and postmortems with clear, actionable follow-ups. • Communicate program status and insights to stakeholders and executives; proactively manage dependencies and risks across a ~25-person cross-functional org and external vendors.