Senior Data Scientist - Clearance Required

Accountabilities:

• Design and develop advanced analytics using Databricks, Python, SQL, Spark/PySpark, statistical techniques, and machine-learning methodologies.
• Build Databricks SQL dashboards, AI/BI dashboards, native visualizations, and other analytical products that provide operational and executive visibility into revenue-cycle performance.
• Develop interactive dashboards for healthcare, financial, coding, revenue-cycle, operational, and executive stakeholders.
• Translate analytical models into intuitive visualizations that highlight financial exposure, recovery opportunities, trends, root causes, anomalies, and recommended priorities.
• Create command-level SITREP dashboards using Healthy, At Risk, and Critical indicators across Front Office, Middle Office, and Back Office revenue-cycle processes.
• Develop drill-down analytics spanning enterprise and network levels through individual facilities, departments, providers, encounters, claims, and claim lines.
• Design analytical models to identify and quantify revenue leakage and potential recovery opportunities across the end-to-end revenue cycle.
• Analyze encounter, documentation, coding, charge, claim, denial, adjudication, payment, and accounts-receivable data to identify patterns associated with lost or delayed revenue.
• Develop detection logic for missing charges, uncoded or delayed encounters, coding inconsistencies, claims-readiness defects, denied or rejected claims, underpayments, unmatched remittances, aged claims, eligibility failures, and authorization issues.
• Create recoverability and priority-scoring models based on financial value, probability of recovery, aging, filing or appeal deadlines, and operational severity.
• Develop payer-performance and denial analytics to identify recurring payer behaviors, denial patterns, reimbursement differences, and process failures.
• Build predictive and anomaly-detection models that identify revenue-cycle problems before they result in lost revenue or excessive Days-to-Bill.
• Establish standardized baselines, KPIs, and analytical measures across Front Office, Middle Office, and Back Office processes.
• Develop financial-impact methodologies for estimating potentially recoverable revenue while maintaining clear separation between analytical estimates and official accounting determinations.
• Support the Revenue Opportunity Ledger by defining analytical fields such as estimated recoverable amount, recoverability score, priority score, root cause, and recommended next action.
• Create dashboard experiences that connect aggregate performance metrics to underlying opportunities and actionable work queues.
• Partner with Data Engineers and Architects to ensure Bronze, Silver, and Gold data structures meet analytical, visualization, and performance requirements.
• Optimize analytical queries and calculations to support responsive, enterprise-scale Databricks dashboards.
• Validate models, KPIs, and dashboard calculations against authoritative source records to ensure accuracy, consistency, and auditability.
• Develop analytical data products supporting coding audits, payer scoring, denial management, revenue recovery, financial reconciliation, and audit remediation.
• Document model objectives, features, methodology, validation, performance, refresh cadence, limitations, and version history.
• Support model monitoring, validation, retraining, and ModelOps activities within an iterative Agile product-development environment.

Requirements:

• 8+ years of professional experience in data science, advanced analytics, quantitative analysis, machine learning, or a related discipline.
• Strong hands-on experience with Databricks and its native analytics and visualization capabilities.
• Demonstrated experience developing Databricks SQL and/or AI/BI dashboards for operational, analytical, and executive audiences.
• Advanced proficiency in Python and SQL, with experience using Spark/PySpark or comparable distributed-computing technologies.
• Proven experience developing predictive models, anomaly-detection systems, classification models, prioritization or scoring models, or comparable analytical capabilities.
• Strong knowledge of feature engineering, model validation, statistical testing, and analytical quality assurance.
• Experience working with complex healthcare, financial, operational, claims, payment, transactional, or similarly structured enterprise datasets.
• Ability to translate business and operational challenges into measurable analytical hypotheses and production-ready data products.
• Strong ability to communicate complex analytical findings through clear visualizations and dashboards for both technical and non-technical audiences.
• Experience defining KPIs and analytical measures that reconcile accurately to authoritative source systems.
• Understanding of modern lakehouse architectures, including Bronze, Silver, and Gold data layers.
• Ability to collaborate effectively with Data Engineers and Architects to define the structures and data products required for analytics and visualization.
• Experience developing auditable, explainable analytical methodologies suitable for financial, regulated, healthcare, or government environments.
• Ability to work effectively within Agile, product-oriented, and iterative delivery environments.
• Ability to comply with applicable government requirements related to security, privacy, access, and sensitive data handling.
• U.S. citizenship is required because the position requires eligibility for a U.S. Government security clearance.
• Prior experience with Advana and/or the War Data Platform (WDP) is preferred.
• Experience working within a Department of Defense Databricks environment is highly desirable.
• Familiarity with Databricks Unity Catalog, Delta Lake, Databricks SQL, AI/BI Dashboards, MLflow, Workflows, or related capabilities is a plus.
• Healthcare revenue-cycle experience involving coding, claims, charge capture, denials, accounts receivable, remittance, payer reimbursement, or underpayment analysis is highly desirable.
• Familiarity with healthcare payer transaction formats such as 835, 837, 270/271, 276/277, and 278 is beneficial.
• Experience with MHS GENESIS, Oracle Health/Cerner Millennium, Abacus, or comparable healthcare information systems is a plus.
• Experience supporting federal financial management, audit remediation, or revenue-related initiatives is advantageous.
• Familiarity with certified data products, data lineage, governance, and data-quality controls is preferred.
• Experience developing explainable AI or machine-learning capabilities in regulated or government environments is a strong advantage.

Benefits:

• Target annual salary: $140,375–$185,604, with final compensation based on skills, experience, education, certifications, and other relevant factors.
• Remote position within the United States.
• Opportunity to contribute to a major federal healthcare analytics and revenue-cycle modernization initiative.
• Opportunity to work with advanced Databricks, machine learning, predictive analytics, and AI/BI technologies.
• Exposure to complex healthcare, financial, claims, and operational datasets at enterprise scale.
• Opportunity to develop analytical products and dashboards with direct operational and financial impact.
• Collaboration with data engineers, healthcare subject-matter experts, government stakeholders, and product leadership.
• Work in a mission-driven environment focused on delivering innovative technology and measurable outcomes for government programs.
• Opportunity to apply data science expertise to regulated, high-impact healthcare and federal environments.
• U.S. Government security clearance eligibility and support for mission-critical work, subject to applicable requirements.