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

Role Overview

We are seeking a Senior Product Manager to lead the strategy and execution of data products, AI/ML systems, AI-powered tooling, and automation initiatives across Patient Experience and Commercial Operations. 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 role in supporting initiatives to reduce friction, improve patient trust, and increase operational efficiency, ultimately driving better conversion, collections, and experience.

You will partner closely with Patient Experience (e.g., digital products, billing, support channels, phlebotomy, and patient services) and Commercial Operations (e.g., customer success, case management, clinical coordination, and workflow operations) teams to identify bottlenecks, unlock scale, and deliver measurable outcomes. Operating in an embedded model, you will align with business stakeholders while building through centralized Data & AI platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver impact across experience, operations, and revenue.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and emerging AI patterns (e.g., LLMs, agentic systems), 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) into clear product direction and measurable outcomes

Discovery, Experimentation, & Requirements

• 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 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

• Define and implement AI product patterns, including agentic workflows and RAG

• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)

• Partner with engineering to implement prompt strategies, guardrails, and continuous evaluation pipelines

• Drive build vs. buy decisioning and proofs of concept

Governance, Compliance, & Data Quality

• Ensure data 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, forecasting accuracy)

• Influence stakeholders across Patient Experience and Commercial Operations without direct authority

Qualifications

Required

• 7–10+ years of product management experience, including 3+ years building data products, AI/ML systems, or related platforms

• Demonstrated domain fluency in Patient Experience and Commercial Operations

• Strong understanding of patient journeys, support channels (e.g., chat, voice, call centers), and operational workflows that drive conversion and test completion

• Familiarity with key domain-relevant tooling (e.g., patient portals and support platforms) and associated data 

• Strong technical acumen (e.g., querying data, working in notebooks for EDA and experimentation, modeling datasets)

• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)

• Experience shipping LLM-powered or AI products (prompting, RAG, evaluation frameworks, guardrails)

• Familiarity with modern data stacks (e.g., Snowflake, dbt, AWS/SageMaker, Airflow/Dagster)

• Strong stakeholder leadership and communication skills, with the ability to work across technical and business teams

• Experience working in regulated environments (e.g., healthcare, life sciences, fintech)

Preferred

• Advanced degree in a technical and/or business discipline

• Experience in healthcare, diagnostics, life sciences, and/or digital health

• Familiarity with healthcare data domains and systems, including patient, provider, payer, claims, billing, and clinical trial data

• Experience with vendor evaluation, platform selection, or external AI tooling ecosystems

What Success Looks Like

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows

• Measurable improvements in key business metrics (e.g., case completion turnaround time, patient satisfaction, cost-to-serve)

• Reliable, high-quality data foundations connect patient, provider, clinical, operational, and billing/financial insights end-to-end

• AI systems are continuously monitored, evaluated, and improved with measurable business impact

• Internal teams have transparent access to the function’s roadmap, trust in delivery against it, and visibility into the evaluation logic that prioritizes initiatives based on their impact to organizational goals