Data Scientist, Revenue Cycle

Jobgether; United States; Remote; USD 73,000 - 117,000; Full time

Benefits offered by this job

Remote work flexibility

Comprehensive benefits

Meaningful healthcare impact

Advanced analytics environment

Cross-functional collaboration

Professional development

Equal employment opportunity

Job summary

Jobgether is seeking a Data Scientist, Revenue Cycle to leverage advanced analytics and ML to optimize healthcare billing, coding, and reimbursement outcomes in a United States setting. You will build predictive models, dashboards, and experiments to reduce denials and revenue leakage while collaborating with finance, clinical, and IT stakeholders.

Work with EHR data and payer data to drive measurable improvements in accounts receivable performance, cash collection, and patient financial

Qualifications

• Master's degree and relevant experience, or bachelor's with 3+ years.
• Strong data analytics, statistical modeling, and ML knowledge.
• Proficiency in Python or R and SQL.
• Experience with Snowflake and data visualization tools.
• Healthcare revenue cycle knowledge and Epic/EHR familiarity.

Responsibilities

• Data Analysis and Revenue Optimization: Query, process, and analyze datasets from EHRs, billing, and financial systems to find revenue leakage and improvement opportunities.
• Predictive Modeling and Machine Learning: Develop, validate, and deploy models to improve denial management and cash collections.
• Revenue Cycle Strategy and Collaboration: Partner with revenue cycle, finance, and operations leaders to support data-driven decisions.
• Performance Monitoring and Visualization: Design dashboards and reports to monitor AR days, denial rates, and net revenue.
• Analytical Testing and Experimentation: Design pilot programs and experiments to evaluate strategies and drive continuous improvement.
• Payer and Claims Intelligence: Identify trends in payer performance and denial drivers to inform interventions.
• Strategic Insights and Communication: Translate findings into actionable recommendations for executives and cross-functional teams.
• Revenue Cycle Transformation: Lead analytics projects for large-scale operational change and workflow optimization.
• Data Quality and Reporting Enhancement: Improve data quality and reporting capabilities with IT and data teams.
• Model Performance and Continuous Improvement: Refine methods and apply new data science techniques as needed.

Skills

Data analytics

Statistical modeling

Machine learning

Python/R

SQL

Tableau

Healthcare revenue cycle

Epic EHR

Communication

Project management

Cloud (AWS/Azure)

Education

Master's degree

Bachelor's + 3 years

Tools

Snowflake

AWS

Azure

Tableau

Git

Job description

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.
• 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.