Head of Informatics

Chamber Cardio•Washington, DC

20d•Hybrid

About The Position

Cardiovascular disease remains the leading cause of death in America. At Chamber, we’re rebuilding the system for cardiology, creating a world where outcomes, not volume, define success. We partner with independent cardiologists to help them lead population health efforts in their communities, equipping them with technology, data, and operational tools that turn complex insights into better care for every patient. Our model blends clinical expertise, thoughtful design, and a modern operating platform that supports physicians, patients, and payers alike. We believe innovation and empathy go hand in hand, and that by combining cutting-edge AI tools with a relentless focus on human care, we can transform heart health at scale. We’re looking for a Head of Informatics to build and lead our clinical data backbone and cardiology brain. You will manage backend clinical ontologies, design and scale our clinical and operations rules engine, and lead development of the clinical program recommendation engine. You combine deep clinical informatics and VBC experience with strong applied data science and statistics, turning complex cardiology and care-management concepts into production-grade data models, algorithms, and decision-support tools. While reporting to the Chief Technology Officer, you’ll work closely with our Chief Medical Officer and Head of Product to build the decision support pathways that underpin our clinical model. This is a hands-on role for a leader who loves building from the ground up, thrives in bringing clarity to ambiguity, and sees AI as a force multiplier for clinical impact.

Requirements

10+ years in bioinformatics, data science, or advanced analytics in a healthcare field.
5+ years working in value-based care environments (e.g., ACOs, risk-bearing provider groups, health plans, or specialty VBC organizations).
Strong applied data science and statistics skills including predictive modeling, risk scoring, recommendation techniques, and observational study design.
Proficiency in Python or R and SQL; experience evaluating and monitoring models (AUC, calibration, lift, stability/drift).
Hands-on experience with large healthcare datasets and converting them into production features and models.
Experience designing clinical data models, cohorts/registries, and clinical decision support or rules engines.
Proven ability to translate clinical and operational requirements into production-ready data and workflow logic with engineering teams.

Responsibilities

Set and own the informatics and data science roadmap aligned to cardiology VBC outcomes (TCoC, utilization, quality, and patient outcomes).
Lead cross-functional efforts with Clinical, Operations, Product, and Engineering from problem framing through implementation and iteration.
Communicate model and rules-engine performance, risks, and roadmap to executives and clinical leaders.
Define and maintain unified clinical concepts, cohorts, and event definitions across claims, EHR, and other data sources in partnership with other technology team members.
Architect and refine the rules engine that operationalizes cardiology programs with a key focus on recommending patient specific care pathways.
Translate clinical program design into executable rules, scores, and triggers that integrate into product, APIs, and care-team workflows.
Lead design and development of a recommendation engine that suggests programs, interventions, and next-best-actions for cardiology patients and providers.
Own the applied data science strategy (feature design, model selection, evaluation, and monitoring) and partner with Data Science/ML and Engineering to deploy models into production.
Monitor performance (calibration, drift, bias, clinical relevance) and iterate based on outcomes and clinician feedback; identify safe, high-value uses of AI/LLMs.
Define processes and standards for informatics and data science work suitable for our evolving startup environment.
Partner with Engineering/MLOps on analytics and ML infrastructure requirements.
Initially operate as a hands-on leader; over time, recruit and mentor a small, high-leverage team of informaticists, analysts, and data scientists.