Senior Product Data Analyst, AI Health Cloud

Our Company

BrightSpring Health Services

Overview

The Senior Product Data Analyst, AI Health Cloud serves as the quantitative and interoperability backbone of the AI Health Cloud platform. You will define and operationalize product metrics, experimentation frameworks, and analytic outputs that drive product strategy and responsible AI execution.

 

You will also serve as the Interoperability SME, bringing deep expertise in healthcare data exchange standards such as FHIR, HL7 v2, C-CDA/CCD, X12, and payer–provider integration patterns. You will work cross‑functionally across product, engineering, data science, clinical, and regulatory teams to ensure the platform delivers accurate analytics, trusted AI, and high‑quality data integration.

Responsibilities

Product Strategy & Metrics

• Define the product’s North Star Metric and supporting metric framework.
• Translate product strategy into measurement plans and telemetry requirements.
• Conduct opportunity sizing, scenario modeling, and impact analyses that guide roadmap decisions.

Interoperability & Data Integration Leadership

• Serve as the Interoperability SME for the AI Health Cloud platform.
• Provide expert guidance on FHIR resources and profiles, SMART on FHIR, HL7 v2 interfaces, C‑CDA/CCD, X12 transactions, and related healthcare integration workflows.
• Validate data quality, mapping accuracy, and schema alignment across multi-source healthcare data feeds (claims, EMR/EHR, registries, clinical quality feeds).
• Partner with engineering and architecture to ensure interoperability pipelines support analytics, AI, and regulatory needs.
• Ensure integrations meet healthcare data and privacy regulations (HIPAA, CMS, NCQA).

Technical Analytics Leadership

• Collaborate with engineering and data science to design event schemas, data contracts, and telemetry instrumentation.
• Contribute analytic requirements to pipelines, data models, and semantic layers supporting the AI Health Cloud.
• Partner with ML Engineering to define and monitor model performance (drift, accuracy, calibration, fairness, hallucination rate, latency, cost-to-serve).

Requirements, Experimentation & Delivery

• Translate stakeholder input into analytic specifications, measurement plans, and experiment designs.
• Lead A/B tests and causal analyses to evaluate feature and model impact.
• Support Agile delivery by ensuring telemetry readiness, data quality, and measurement alignment for every release.

Quality, Compliance & Responsible AI

• Ensure analytics and data flows comply with regulatory and responsible AI standards.
• Build monitoring systems to detect drift, data quality issues, mapping errors, and anomalous model behavior.
• Maintain audit-ready documentation for data lineage, quality, and AI governance.

Analytics, Insights & Product Health

• Build dashboards and analyses to measure product health (usage, latency, reliability, safety, data quality).
• Conduct deep-dive analyses and root-cause investigations across product, model, and data domains.
• Deliver clear, executive-ready insights and recommendations backed by analytic rigor.

Stakeholder Engagement

• Act as senior analytics and interoperability liaison for internal and external partners.
• Facilitate feedback loops to improve data quality, model performance, and workflow efficiency.
• Present findings, KPIs, and experiment outcomes to cross-functional leadership groups.

Qualifications

• 10+ years in analytics, data science, interoperability, product analytics, or related healthcare data roles.
• Extensive experience with FHIR (R4 preferred), HL7 v2, C-CDA/CCD, X12, and clinical/claims data integration.
• Expert-level proficiency with SQL and Python/R.
• Strong background in data modeling, event instrumentation, and semantic layer design.
• Deep understanding of key healthcare data sources (claims, EMR/EHR, quality programs, registries).
• Experience in regulated healthcare environments with HIPAA, CMS, or NCQA.
• Exceptional communication and stakeholder leadership ability.

Preferred Qualifications

• Experience with cloud ecosystems (Azure, GCP, AWS) and interoperability frameworks (FHIR servers, integration engines, API gateways).
• Familiarity with MLOps and AI model monitoring tools.
• Proficiency with BI tools (Power BI, Tableau, Da