Data Scientist

Cadence is the clinical AI company automating the treatment of chronic disease. Its Clinical Intelligence takes on the routine, high-volume work of chronic care – monitoring patients, surfacing risks, and coordinating action – so clinicians can focus on the decisions that require them. Grounded in partnerships with more than 20 leading health systems, deep EMR integrations, dedicated medical group, and experience serving a population of more than 100,000 patients, the system learns from real-world care delivery, improving as it scales. Cadence has been recognized by TIME as one of the Top 100 HealthTech Companies and by LinkedIn as a Top Startup (#4, 2025).

The Role

We're hiring a Staff Data Scientist to define the KPIs, data models, and analytical infrastructure that support Cadence's product, operational, and clinical decisions for our Advanced Primary Care Management business unit. You will partner closely with product management, operations, care delivery, and customer success to build a scalable analytics roadmap and translate complex data - including patient vitals, EHR records, and clinical outcomes - into insights that drive strategy. This role sits at the center of how Cadence measures and improves care delivery at scale.

What You'll Do

• Own the analytical strategy for Cadence's digital primary care program end-to-end by defining what outcomes and KPIs matter, diagnosing ambiguous operational and clinical problems, and serving as the primary thought partner to clinical and operational leadership; exploring patient vitals, EHR data, and clinician-generated data to surface insights that close care gaps, drive better outcomes, and inform program strategy.
• Define and communicate outcome frameworks for AI-powered primary care, translating clinical and operational performance into clear, credible narratives for external partners including health systems and payers, and serving as the analytical voice in those conversations.
• Design and build core data models in dbt from the ground up, establishing canonical metric definitions, data collection standards, and the structural foundation that the broader team relies on; maintain the modern data stack (Snowflake, Fivetran, dbt) to keep infrastructure accurate, well-documented, and built to scale.
• Deliver advanced analytics across the full stack from pipeline construction and data engineering to predictive modeling, staffing and capacity forecasting, and revenue modeling in partnership with operations and executive leadership.
• Build and maintain reports and dashboards that monitor growth, engagement, clinical outcomes, and Cadence's demonstrated value to patients and health system partners with metrics that are trustworthy and tied to decisions.
• Embed AI meaningfully into the analytics function by identifying high-value use cases, building reusable workflows and automation, and leading adoption of LLM-assisted tools that reduce manual overhead and expand what the team can produce.