• Own the patient communications roadmap across SMS and email — prescription status updates, refill reminders, onboarding flows, and beyond • Build the foundation for patient journey mapping: define lifecycle stages, segment patients by behavior and need, and design tailored communication tracks for each • Design and run A/B tests and experiments to optimize message timing, content, cadence, and channel — with a clear framework for what we learn and how we act on it • Ensure communications are deeply integrated with the patient portal and checkout flow, not bolted on — the in-app and out-of-app experience should feel like one journey • Partner with engineering to build on top of Twilio, understanding its capabilities and constraints well enough to make sound product and architecture decisions • Define the metrics that matter — open rates, click-through, on-treatment rates, support deflection — and drive toward them • Work with clinical and compliance to ensure all communications meet regulatory requirements • Collaborate with design and content to make every message feel warm, clear, and on-brand

Role Description We are seeking a Mobile Product Owner to lead the execution and delivery of AI-driven features, use cases, and platform capabilities across a suite of SaaS products. This role requires strong collaboration with engineering, data science, and cross-functional product teams to ensure successful integration of AI and automation into mobile experiences and operational workflows. - Drive the end-to-end execution and delivery of AI-powered features, use cases, and platform enhancements across mobile SaaS products - Translate business objectives and product needs into clear technical requirements, user stories, and sprint deliverables for engineering and data science teams - Partner with cross-functional squads to embed AI and automation within existing workflows such as recruiting, onboarding, performance management, learning, analytics, and public safety - Own and prioritize the product backlog for AI feature development, balancing quick wins with long-term scalability and platform evolution - Define and maintain execution roadmaps, timelines, and milestones for model training, integration, and release across multiple product teams - Monitor delivery KPIs including velocity, cycle time, release quality, and adoption metrics, and drive ongoing improvements in execution efficiency - Support post-deployment validation efforts to ensure model accuracy, system reliability, and optimal user experience Company Description

Role Description Drive the execution and delivery of AI-powered features, use cases, and platform capabilities across a suite of SaaS products. - Translate business and product team needs into concrete technical requirements, stories, and sprint deliverables for engineering and data science teams. - Coordinate with cross-functional squads to embed AI and automation into existing workflows (e.g., recruiting, onboarding, performance, learning, analytics, and public safety). - Define and manage the backlog for AI feature delivery, balancing short-term wins with long-term platform scalability. - Establish execution roadmaps and milestones for model training, integration, and release across multiple product teams. - Track delivery KPIs (velocity, cycle time, release quality, adoption metrics) and drive continuous improvement in execution efficiency. - Support post-deployment validation to ensure model accuracy, system reliability, and user experience alignment. Company Description

• Define and execute the long-term product strategy for Docker's internal developer platform and AI developer tooling, spanning infrastructure, self-service, CI/CD, and AI-powered and agentic workflows. • Drive an integrated roadmap that balances foundational platform investments with the AI-native capabilities that differentiate Docker. • Align platform and tooling strategy with Docker's business objectives and product portfolio, in partnership with engineering and executive leadership. • Treat the internal platform as a product: define golden paths, paved roads, and self-service capabilities that let teams provision, deploy, observe, and operate with minimal friction and strong guardrails. • Establish clear contracts, defaults, and documentation, and drive adoption through measurable outcomes rather than mandate. • Partner with infrastructure teams to translate reliability, scale, networking, and cost priorities into a roadmap teams can trust. • Shape where AI agents and assisted workflows earn their place across the SDLC and operations, from code authoring and review to incident response, with a bias toward safe, auditable, human-reviewed automation. • Define how AI tooling effectiveness is measured: adoption, productivity gains, and developer satisfaction. • Identify which internal tools and platform capabilities are ready to become customer-facing offerings, and own the strategy that takes them from prototype to product.