Data Scientist, Revenue Cycle

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Scientist, Revenue Cycle based in the United States. The Data Scientist, Revenue Cycle will leverage advanced analytics, statistical modeling, and machine learning to improve financial performance and operational efficiency across a complex healthcare environment. This role focuses on transforming large datasets into actionable insights that optimize billing, coding, claims processing, collections, and reimbursement outcomes. Working closely with revenue cycle leadership, finance teams, clinical stakeholders, and technology partners, you will identify opportunities to reduce denials, prevent revenue leakage, and improve the patient financial experience. You will develop predictive models, design analytical experiments, and build dashboards that support data-driven decision-making at both operational and executive levels. The position combines technical expertise with practical business impact, contributing to revenue cycle transformation and continuous process improvement. This is an opportunity to apply innovative data science methods to meaningful healthcare challenges within a collaborative, mission-driven organization. Accountabilities: - Data Analysis and Revenue Optimization: Query, process, and analyze large, complex datasets from electronic health records (EHRs), billing platforms, claims systems, and financial databases to identify revenue leakage, performance gaps, and opportunities for improvement. - Predictive Modeling and Machine Learning: Develop, validate, and deploy predictive models that enhance denial management, improve cash collections, optimize reimbursement, and support more effective revenue cycle decisions.

- Revenue Cycle Strategy and Collaboration: Partner with revenue cycle, finance, and operational leaders to support data-informed decision-making across billing, coding, claims management, collections, and related financial processes. - Performance Monitoring and Visualization: Design and maintain dashboards, reports, and data visualizations to monitor key performance indicators, including accounts receivable (AR) days, denial rates, net revenue performance, and other operational and financial metrics. - Analytical Testing and Experimentation: Design and implement pilot programs and analytical experiments to evaluate revenue cycle strategies, measure their effectiveness, and identify opportunities for continuous improvement in real-world operating environments. - Payer and Claims Intelligence: Identify trends and patterns in payer performance, coding accuracy, claim adjudication, reimbursement processes, and denial drivers to inform targeted interventions and improve financial outcomes. - Strategic Insights and Communication: Translate complex analytical findings into clear, actionable recommendations for operational leaders, executives, and non-technical stakeholders, supporting evidence-based planning and prioritization. - Revenue Cycle Transformation: Lead data science and analytics projects that support large-scale operational transformation, workflow optimization, and sustainable performance improvements. - Data Quality and Reporting Enhancement: Collaborate with IT, clinical, and data teams to improve data quality, strengthen reporting capabilities, and ensure analytical outputs are accurate, reliable, and relevant to business needs. - Model Performance and Continuous Improvement: Evaluate predictive model accuracy and effectiveness, refine analytical methodologies, and identify opportunities to apply emerging data science techniques to evolving healthcare financial challenges. Requirements: - Education and Experience: A master's degree with at least some relevant experience, including entry-level candidates, or a bachelor's degree with a minimum of three years of relevant professional experience.