Sr. Technical Product Manager - Data & Applied AI (Patient Experience & Commercial Operations)

Role Overview We are seeking a Sr. Technical Product Manager to lead the strategy and execution of data product, AI/ML system, AI-powered tooling, and automation initiatives across Patient Experience and Commercial Operations functions. This role is focused on improving how patients navigate testing and billing while enabling operational teams to efficiently progress cases from order to completion. You will play a crucial part in supporting initiatives to reduce friction, improve patient trust, and increase operational efficiency, ultimately driving better conversion, collections, and experience. This role focuses on building, scaling, and leveraging platforms and products that power decision intelligence and activity orchestration across these domains. You will work in close collaboration with domain leaders while building through centralized Data & AI organization platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver measurable business impact. In doing so, you will work hands-on as an empowered builder of AI and software solutions to improve workflow efficiency, productivity, and quality for the teams you support. This is a technical product role requiring fluency in data systems, modern data platforms, ML, and AI implementation patterns (and the ability and drive to build with them directly), combined with strong experience and stakeholder intuition across the associated functions. The role has direct ownership of high-impact initiatives that deeply influence organizational success. What You’ll Do Strategy & Roadmap Define and own the Data & AI product strategy and roadmap for the Patient Experience + Commercial Operations pod by deeply partnering with business leaders to proactively identify high-impact opportunities, shape problem definitions, and drive aligned priorities Translate ambiguous business problems (e.g., stuck cases, fragmented patient communication, support inefficiencies, billing friction) into clear product direction and measurable outcomes Own the line between 'what the business needs' and 'what gets built' end-to-end Discovery, Experimentation, & Requirements Develop deep context on the domain so you can proactively propose solutions rather than field requests Be hands-on with data: query datasets, review schemas, and validate assumptions through analysis Lead end-to-end product discovery with interviews, workflow mapping, data assessments, ROI modeling, etc. Define clear product requirements (PRDs, user stories, acceptance criteria) and success metrics Design and run experiments to validate product performance and measure causal impact Establish leading indicators and KPIs for proactive product and process health assessments Delivery, Data, & ML Lifecycle Partner with data and AI/ML engineering resources to deliver scalable products and capabilities Guide development of robust data pipelines and unified data models (360° views across key entities) Own the end-to-end ML lifecycle: feature definition, evaluation, deployment, monitoring, drift detection, and retraining Ensure training–serving consistency, model versioning, and clear deployment decision gates Establish strong observability across data pipelines and models (data quality, latency, reliability, cost) AI Productization & Hands-On Building Define and implement AI product patterns, including agentic workflows and tool/function-calling integrations Build process automations and internal tools that improve workflow efficiency, productivity, and output quality for the teams you support Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency) Implement prompt strategies, guardrails, and continuous evaluation pipelines Governance, Compliance, & Data Quality Ensure products meet regulatory and compliance requirements Champion data quality, lineage, and reliability through data contracts and observability standards Maintain strong documentation practices (e.g., model cards, dataset documentation, audit trails) Partner with governance teams (Security, Legal, Compliance, AI Governance) to operationalize AI responsibly Adoption, Change Management, & Impact Launch products with supporting enablement activities to ensure solutions are embedded in workflows with confidence Partner with stakeholders to integrate products into proactive, day-to-day decision-making Monitor product usage, performance, and business outcomes to iterate based on data and feedback Quantify and communicate impact (e.g., revenue lift, cost reduction, cycle time improvements, case completion turnaround) Influence Patient Experience and Commercial Operations stakeholders through delivery, not authority Qualifications Required 7+ years of relevant experience (e.g., product management, applied AI/ML engineering, technical founder, f