Principal Data Intelligence Analyst (Clinical Operations) - Hybrid

Cedars-Sinai•United States,

5h•$143,500 - $208,500•Hybrid

About The Position

The Principal Data Intelligence Analyst (Clinical Operations) independently leads strategic data and analytics projects, analyzing complex problems using internal and external data. The role provides consultation to users and leads cross-functional teams addressing data and analytics needs. It supports clinical operations by turning healthcare data and Epic workflows into meaningful measures, actionable insights, and scalable data products. The analyst works with clinical and operational stakeholders to define business questions, measures of success, requirements, and reporting priorities. The role develops and supports analytics, data models, SQL and Python analyses, and Tableau dashboards; validates data quality; develops predictive and prescriptive analytics; and supports end-user training, adoption, documentation, and ongoing use. The position also mentors analysts and provides technical and functional leadership on cross-functional initiatives. Local candidates and candidates willing to relocate to Los Angeles are preferred. Remote candidates may be considered in Arizona, California, Colorado, Florida, Georgia, Minnesota, Nevada, Oregon, or Texas.

Requirements

Ten or more years of experience in data analytics, business intelligence, healthcare analytics, clinical operations, or a related discipline.
Demonstrated experience supporting clinical operations, healthcare performance improvement, or quantitative analysis of quality, efficiency, access, utilization, or productivity measures.
Advanced SQL skills, including complex joins, subqueries, common table expressions, window functions, performance optimization, data validation, and reconciliation.
Strong hands-on Python experience for data analysis, automation, data mining, statistical analysis, and modeling.
Demonstrated experience with Epic Clarity and Epic Caboodle, including understanding Epic data structures and mapping clinical workflows to analytic requirements.
Experience performing the full analytics delivery lifecycle: requirements gathering, data discovery, exploratory analysis, data modeling, development, testing, deployment, documentation, training, and support.
Experience developing predictive or prescriptive analytics.
Experience with Tableau or a comparable enterprise visualization platform.
Bachelor’s degree in Computer Science, Information Technology, Health Informatics, Business Analytics, or a related field.

Nice To Haves

Experience with Microsoft Fabric, including one or more of Fabric Data Factory, Lakehouse, Warehouse, notebooks, pipelines, semantic models, Power BI, or governance capabilities.
Epic Clarity and/or Caboodle Certification.
Local candidates or those willing to relocate to Los Angeles are preferred.

Responsibilities

Lead strategic clinical operations analytics projects involving quality, safety, access, throughput, productivity, utilization, staffing, financial performance, and patient experience.
Partner with clinical and operational stakeholders to define business questions, success measures, analytic requirements, and reporting priorities.
Facilitate consensus-building sessions with physicians, operational leaders, clinical staff, and end users.
Develop complex, optimized SQL queries against enterprise healthcare data, including Epic Clarity and Caboodle.
Extract, transform, validate, and analyze large clinical, operational, and financial datasets.
Use Python for exploratory data analysis, data quality assessment, statistical analysis, automation, data mining, and predictive modeling.
Translate Epic clinical workflows and data structures into meaningful operational measures and actionable insights.
Design data models, reusable analytic datasets, reporting specifications, and scalable data products.
Develop and support Tableau dashboards and other visualization solutions that clearly communicate complex clinical and operational concepts.
Perform requirements documentation, source-to-target mapping, data profiling, testing, quality assurance, and reconciliation with report developers and data engineering teams.
Identify data quality issues, investigate root causes, and establish appropriate validation and governance practices.
Develop predictive and prescriptive analytics to support operational planning, risk identification, resource allocation, and performance improvement.
Lead end-user training, adoption, documentation, and post-deployment support.
Mentor analysts and peers and provide technical and functional leadership on cross-functional initiatives.