This faculty role in pediatric health system sciences bridges hospital operations, health systems science, and applied data science to improve real-time decision-making and measurable performance across the enterprise. This position is embedded with operational leaders and clinical stakeholders to translate complex EHR and operational data into actionable intelligence that supports patient flow, capacity management, staffing resilience, safety, and service-line performance. While the organization maintains enterprise analytics leadership, this role will serve as a hands-on scientific lead focused on operational use cases—building trusted tools and methods that leaders can rely on every day. In the health system, you will design and deliver decision-ready dashboards, scorecards, and rapid-cycle analyses to support daily operational huddles, leadership rounds, and escalation decisions. You will develop and maintain core flow and throughput analytics (e.g., length of stay, discharge readiness/early discharge, avoidable delays, bed turnover, environmental services turnaround, transfer delays, diversion) and build forecasting and scenario models to anticipate demand, staffing needs, and surge events. This is a dual-employment position with the affiliated children's hospital, aligned to a Health Systems Science (HSS) faculty appointment with an intentional effort split between hospital operations, grant-funded faculty research, and teaching/mentorship. The research component focuses on externally funded scholarship that advances operational excellence (e.g., evaluation of interventions, learning health system methods, predictive analytics implementation) in direct partnership with hospital leaders. The teaching component includes supervising graduate assistants embedded in operational analytics projects, building a sustainable pipeline of talent and capability.

Required Qualifications:
PhD in a quantitative discipline

Supplemental Qualifications:
Ideal candidates bring a PhD in a quantitative discipline, strong SQL, statistical modeling, and visualization skills, and the ability to balance scientific rigor with practical, time-sensitive operational needs.

Salary Range: Salary Commensurate with Experience

This faculty role in pediatric health system sciences bridges hospital operations, health systems science, and applied data science to improve real-time decision-making and measurable performance across the enterprise. This position is embedded with operational leaders and clinical stakeholders to translate complex EHR and operational data into actionable intelligence that supports patient flow, capacity management, staffing resilience, safety, and service-line performance. While the organization maintains enterprise analytics leadership, this role will serve as a hands-on scientific lead focused on operational use cases—building trusted tools and methods that leaders can rely on every day. In the health system, you will design and deliver decision-ready dashboards, scorecards, and rapid-cycle analyses to support daily operational huddles, leadership rounds, and escalation decisions. You will develop and maintain core flow and throughput analytics (e.g., length of stay, discharge readiness/early discharge, avoidable delays, bed turnover, environmental services turnaround, transfer delays, diversion) and build forecasting and scenario models to anticipate demand, staffing needs, and surge events. This is a dual-employment position with the affiliated children's hospital, aligned to a Health Systems Science (HSS) faculty appointment with an intentional effort split between hospital operations, grant-funded faculty research, and teaching/mentorship. The research component focuses on externally funded scholarship that advances operational excellence (e.g., evaluation of interventions, learning health system methods, predictive analytics implementation) in direct partnership with hospital leaders. The teaching component includes supervising graduate assistants embedded in operational analytics projects, building a sustainable pipeline of talent and capability.

Required Qualifications:
PhD in a quantitative discipline

Supplemental Qualifications:
Ideal candidates bring a PhD in a quantitative discipline, strong SQL, statistical modeling, and visualization skills, and the ability to balance scientific rigor with practical, time-sensitive operational needs.

Salary Range: Salary Commensurate with Experience