• Requirements gathering and Business logic: You work with design, engineering, clinical, and operations stakeholders to gather requirements and business logic before work begins. In the world of AI coding, you will ensure we have strong documentation of these inputs. You define alongside design what “done” looks like for every feature, set clear acceptance criteria, document edge cases, and get sign-off from the right people. Nothing moves to build without it. • Technical and operational documentation: You write and maintain the documentation that keeps teams aligned. That includes feature specs, process documentation for clinical and ops rollouts, and reference materials for training. You own the documentation lifecycle, not just the first draft. • Launch management: You drive feature launches from testing through release. You coordinate across design, engineering, clinical operations, and training to make sure each launch has clear owners, timelines, and acceptance criteria. You work directly with trainers to ensure clinical and ops staff are prepared before anything goes live. • Measuring post-launch impact and internal user feedback: You own the systems that measure how features perform after they go live. You manage post-launch A/B testing and performance tracking across clinicians, operations staff, and patients. You collect and synthesize internal feedback to identify where the product is meeting goals and where it is falling short, ensuring that data-driven insights directly inform the next iteration of the roadmap. • Decision tracking and roadmap orchestration: You capture what was decided, by whom, and why. You translate those decisions into tickets with clear requirements and acceptance criteria. You track requirements across the full lifecycle, including the ones that surface mid-sprint, and you flag gaps before they create problems downstream. • AI-powered execution: You use AI tools daily to accelerate your own work, writing requirements documentation, synthesizing meeting notes, surfacing action items, drafting release notes, and automating your work. You are not waiting for someone to build a system around you. You build it yourself using the tools available.

• Requirements gathering and Business logic: You work with design, engineering, clinical, and operations stakeholders to gather requirements and business logic before work begins. In the world of AI coding, you will ensure we have strong documentation of these inputs. • Technical and operational documentation: You write and maintain the documentation that keeps teams aligned. That includes feature specs, process documentation for clinical and ops rollouts, and reference materials for training. • Launch management: You drive feature launches from testing through release. You coordinate across design, engineering, clinical operations, and training to make sure each launch has clear owners, timelines, and acceptance criteria. • Measuring post-launch impact and internal user feedback: You own the systems that measure how features perform after they go live. • Decision tracking and roadmap orchestration: You capture what was decided, by whom, and why. You translate those decisions into tickets with clear requirements and acceptance criteria. • AI-powered execution: You use AI tools daily to accelerate your own work, writing requirements documentation, synthesizing meeting notes, surfacing action items, drafting release notes, and automating your work.

• You work with design, engineering, clinical, and operations stakeholders to gather requirements and business logic before work begins. • You write and maintain the documentation that keeps teams aligned, including feature specs, process documentation for clinical and ops rollouts, and reference materials for training. • You drive feature launches from testing through release, coordinating across design, engineering, clinical operations, and training to make sure each launch has clear owners, timelines, and acceptance criteria. • You own the systems that measure how features perform after they go live, managing post-launch A/B testing and performance tracking. • You capture what was decided, by whom, and why, translating those decisions into tickets with clear requirements and acceptance criteria.

• Requirements gathering and Business logic: You work with design, engineering, clinical, and operations stakeholders to gather requirements and business logic before work begins. In the world of AI coding, you will ensure we have strong documentation of these inputs. You define alongside design what “done” looks like for every feature, set clear acceptance criteria, document edge cases, and get sign-off from the right people. Nothing moves to build without it. • Technical and operational documentation: You write and maintain the documentation that keeps teams aligned. That includes feature specs, process documentation for clinical and ops rollouts, and reference materials for training. You own the documentation lifecycle, not just the first draft. • Launch management: You drive feature launches from testing through release. You coordinate across design, engineering, clinical operations, and training to make sure each launch has clear owners, timelines, and acceptance criteria. You work directly with trainers to ensure clinical and ops staff are prepared before anything goes live. • Measuring post-launch impact and internal user feedback: You own the systems that measure how features perform after they go live. You manage post-launch A/B testing and performance tracking across clinicians, operations staff, and patients. You collect and synthesize internal feedback to identify where the product is meeting goals and where it is falling short, ensuring that data-driven insights directly inform the next iteration of the roadmap. • Decision tracking and roadmap orchestration: You capture what was decided, by whom, and why. You translate those decisions into tickets with clear requirements and acceptance criteria. You track requirements across the full lifecycle, including the ones that surface mid-sprint , and you flag gaps before they create problems downstream. • AI-powered execution: You use AI tools daily to accelerate your own work, writing requirements documentation, synthesizing meeting notes, surfacing action items, drafting release notes, and automating your work. You are not waiting for someone to build a system around you. You build it yourself using the tools available.