Data Scientist, Revenue Cycle

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.

• Data Science and Analytics: Demonstrated knowledge of data analytics, statistical modeling, predictive analytics, and machine learning techniques, with the ability to translate analytical results into measurable business outcomes.

• Programming Proficiency: Experience with at least one programming language, such as Python, R, Java, or C/C++, for data manipulation, analysis, modeling, and automation.

• SQL and Database Management: Strong SQL skills and experience working with relational databases, including platforms such as Snowflake, to extract, transform, and analyze complex datasets.

• Data Visualization: Experience developing dashboards, reports, and visualizations using tools such as Tableau, with the ability to communicate insights effectively to technical and non-technical audiences.

• Machine Learning Expertise: Familiarity with machine learning algorithms and frameworks, including Random Forest, XGBoost, and LightGBM, as well as deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs).

• Healthcare Revenue Cycle Knowledge: Familiarity with healthcare revenue cycle data, including billing, coding, claims processing, payer workflows, and reimbursement processes, is required.

• Healthcare Technology Experience: Experience working with electronic health record systems, particularly Epic, and integrating or analyzing data from healthcare operational systems.

• Analytical and Problem-Solving Skills: Strong critical-thinking abilities, attention to detail, and the capacity to extract meaningful insights from complex datasets to solve operational and financial challenges.

• Communication and Stakeholder Management: Ability to present analytical findings clearly, develop actionable recommendations, and collaborate effectively with cross-functional teams, including finance, clinical operations, IT, and executive leadership.

• Project Management: Demonstrated ability to manage complex analytics initiatives, coordinate multiple stakeholders, and deliver high-quality results within established timelines.

• Preferred Technical Skills: Experience with cloud platforms such as AWS or Microsoft Azure and familiarity with modern data science tools, workflows, and deployment practices are preferred.

Benefits:

• Competitive Compensation: Published hourly pay range of approximately $53.29 to $85.26, depending on professional experience, qualifications, and work location.

• Remote Work Flexibility: Nationwide remote position within the United States.

• Comprehensive Benefits: Access to a comprehensive employee benefits package, with specific coverage and eligibility determined by the employer.

• Meaningful Healthcare Impact: The opportunity to apply data science and predictive analytics to improve healthcare financial operations and support a better patient financial experience.

• Advanced Technical Environment: Opportunities to work with large-scale healthcare datasets, machine learning methodologies, modern analytics platforms, and enterprise reporting tools.

• Cross-Functional Collaboration: Work closely with revenue cycle, finance, clinical, and technology professionals to develop and implement data-driven solutions.

• Professional Development: Opportunities to strengthen expertise in machine learning, healthcare analytics, predictive modeling, and revenue cycle optimization.

• Equal Employment Opportunity: A commitment to equal employment opportunities. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1