Senior Vice President of AI & Data, Reveleer

• A track record of leading AI and data organizations, with enough technical depth to evaluate architecture, model-selection, and build-versus-buy decisions made by the teams reporting in. Personally, building production systems is not required
• Deep experience with applied machine learning and large language models in production, including modern patterns such as retrieval-augmented generation, OCR and document understanding, and clinical natural language processing
• Experience leading data warehouse and business intelligence functions, with working knowledge of cloud data platforms (Azure Synapse, Microsoft Fabric, or comparable) and BI tools such as Looker
• Demonstrated leadership of multiple technical teams and their leaders, with a style that sets direction and architecture while granting autonomy on execution
• Working fluency in healthcare risk adjustment, medical coding (ICD), and quality measurement (HEDIS®), or the ability to reach it quickly

Desirable

• Reveleer is hiring an SVP, AI & Data to lead the strategy, architecture, and delivery of the AI, machine learning, and data systems that power risk adjustment and quality programs across Medicare Advantage and value-based care
• The role owns the platform that extracts ICD and HEDIS® information from medical charts, the data warehouse and business intelligence layers that surface that work, and the data science research that advances accuracy over time
• This is a working executive role. The SVP, AI & Data sets technical direction and architecture, partners with engineering and product leadership on delivery, and stays close enough to the systems to make sound build decisions — while giving team leaders the autonomy to own their domains
• The position leads seven teams totaling roughly 27 engineers, data scientists, and analysts through their respective leaders
• AI and machine learning for chart intelligence:
• Direct the engineering teams that extract medical codes and quality data from charts: retrospective risk extraction built on Textract OCR, Comprehend Medical, and Gemini; prospective risk extraction that runs PDF and CCDA charts through Gemini and a custom rules engine to surface candidate ICD codes; and quality and HEDIS extraction that applies large language models to chart content
• Own the internal API that returns extraction results to downstream engineering teams, including its contracts, accuracy, and reliability
• Guide the data science research program — a team including two PhDs and an MD — as it advances from model-based ICD extraction to a retrieval-augmented generation system on Gemini, and shepherd that research into production alongside the retrospective risk team
• Data warehouse and business intelligence:
• Set the architecture for the data warehouse on Azure Synapse and Microsoft Fabric, including ingestion from AWS SQL source databases
• Direct the business intelligence function that builds reporting in Looker on top of warehouse data, with quality assurance built into the delivery process
• Risk analytics:
• Oversee risk analytics expertise in determining Medicare Advantage value from coded medical charts, supporting a substantial share of the IVA business
• Core responsibilities:
• Technical strategy and architecture. Define the architecture for the AI, data, and analytics platform end to end, and make the build-versus-buy and model-selection decisions that shape it
• Innovation direction. Provide the guiding voice on innovation across all teams, evaluating new approaches in extraction, retrieval, and clinical AI while preserving each team’s autonomy to decide how to execute
• Delivery and reliability. Hold the teams accountable for accuracy, throughput, and uptime of the extraction of APIs and the data and reporting layers that depend on them
• Leadership and org development. Lead seven team leaders, grow technical talent, and redeploy team capacity toward higher-value clinical and research work as automation matures
• Cross-functional partnership. Work with product, engineering, and clinical leadership to align the AI and data roadmap with commercial and regulatory priorities
• Governance and compliance. Establish data governance, model evaluation, and privacy practices appropriate to protected health information and CMS-regulated programs
• How success is measured:
• Accuracy and coverage of code and quality extraction across retrospective, prospective, and HEDIS® workflows
• Reliability and adoption of the internal results API by downstream engineering teams
• Timeliness and trustworthiness of warehouse data and the reporting built on it
• Movement of data science research into production, measured by accuracy gains realized in live extraction
• Growth, retention, and autonomy of the team leaders and their teams