Senior Data Scientist - Clearance Required

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist - Clearance Required based in the United States.

This is a senior data science opportunity supporting a major federal healthcare revenue-cycle modernization initiative.

You will transform complex healthcare, financial, claims, payment, coding, and operational data into actionable intelligence.

The role combines advanced analytics, machine learning, statistical modeling, and data visualization within a Databricks-based environment.

Your work will help identify revenue leakage, coding and charge-capture issues, denied claims, underpayments, aged receivables, and recovery opportunities.

You will collaborate closely with data engineers, healthcare and revenue-cycle experts, government stakeholders, and product leaders.

The position goes beyond reporting, requiring you to build analytical products and dashboards that reveal root causes, prioritize interventions, and measure results.

This is an ideal role for an experienced data scientist who enjoys solving complex problems in regulated environments and delivering measurable mission impact.

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.

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.