OverviewThe Senior Data Scientist will support the Defense Health Agency (DHA) Revenue Cycle Operating System (RevOS) initiative by designing and implementing advanced analytics, statistical models, predictive capabilities, and decision-support visualizations within a Databricks-based environment.
The role will focus on transforming complex healthcare, financial, coding, claims, payment, and operational data into actionable intelligence that enables DHA to identify revenue leakage, coding and charge-capture errors, denied or stalled claims, underpayments, aged receivables, and opportunities to recover revenue.
The Senior Data Scientist will work closely with Data Engineers, Revenue Cycle SMEs, DHA stakeholders, and product leadership to develop analytics aligned to the end-to-end revenue-cycle workflow:
Scheduling 12 Eligibility 12 Registration 12 Authorization 12 Patient Care 12 Documentation 12 Coding 12 Charge Capture 12 Claims 12 Adjudication 12 Remittance 12 Denials 12 Collections / RecoveryThe objective is not simply to produce reports. The role will help create analytical products and
Databricks-based dashboards that identify where revenue-cycle processes are failing, quantify financial impact, prioritize corrective action, and measure recovery.
Responsibilities- Design and develop advanced analytics within Databricks using Python, SQL, Spark/PySpark, statistical methods, and machine-learning techniques.
- Develop Databricks visualizations, Databricks SQL dashboards, AI/BI dashboards, and related native visualization capabilities to provide operational and executive visibility into RevOS performance.
- Create interactive dashboards supporting DHA J-8, DHN, MTF, revenue-cycle, coding, financial, and executive users.
- Translate analytical models into intuitive visualizations showing financial exposure, recovery opportunity, trends, root causes, outliers, and recommended operational priorities.
- Develop command-level RevOS SITREP dashboards using Healthy / At Risk / Critical indicators across the Front, Middle, and Back Office revenue cycle.
- Develop drill-down analytics from enterprise and DHN levels through MTF, department, provider, encounter, claim, and claim-line levels.
- Design and develop analytical models that identify and quantify potential revenue leakage and recovery opportunities.
- 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 or incomplete charges
- Uncoded and delayed encounters
- Coding inconsistencies and potential coding errors
- Denied and rejected claims
- Underpayments and unexplained payment variances
- Unmatched or unposted remittances
- Aged claims and receivables
- Eligibility and authorization failures
- Develop recoverability and priority-scoring models based on financial value, probability of recovery, aging, filing/appeal deadlines, and operational severity.
- Develop payer-performance and denial analytics to identify recurring payer behaviors, denial patterns, reimbursement variances, and process failures.
- Build predictive models that identify revenue-cycle failures before they result in lost revenue or excessive Days-to-Bill.
- Establish baselines and anomaly-detection methodologies across Front Office, Middle Office, and Back Office processes.
- Design financial-impact methodologies that estimate potentially recoverable revenue while maintaining separation between analytical estimates and official accounting determinations.
- Develop and validate standardized RevOS KPIs and analytical measures.
- Support development of the RevOS Revenue Opportunity Ledger, including estimated recoverable amount, recoverability score, priority score, root cause, and recommended next action.
- Create dashboard views that allow users to move from aggregate metrics into the underlying Revenue Opportunity Ledger and actionable work queues.
- Partner with Data Engineers to ensure Silver and Gold structures support analytical, visualization, and dashboard performance requirements.
- Optimize analytical queries and calculations used by Databricks dashboards to support responsive enterprise-scale visualization.
- Validate that models, KPIs, and dashboard calculations reconcile to authoritative source records.
- Develop analytical data products supporting coding audit, payer scoring, denial management, revenue recovery, financial reconciliation, and audit remediation.
- Document model purpose, features, methodology, validation, performance, refresh cadence, limitations, and version history.
- Support model monitoring, validation, retraining, and ModelOps practices.
QualificationsRequired Qualifications - 8+ years of professional experience in data science, advanced analytics, quantitative analysis, machine learning, or related disciplines.
- Strong hands-on experience with Databricks.
- Demonstrated ability to use Databricks native visualization and dashboard capabilities, including Databricks SQL and/or AI/BI dashboards.
- Experience designing operational, analytical, and executive dashboards based on large enterprise datasets.
- Advanced proficiency with Python and SQL.
- Experience with Spark/PySpark or comparable distributed-computing technologies.
- Demonstrated experience developing predictive models, anomaly detection, classification, prioritization/scoring models, or similar analytical capabilities.
- Strong understanding of feature engineering, model validation, statistical testing, and analytical quality assurance.
- Experience working with complex financial, operational, healthcare, claims, payment, or transactional data.
- Ability to translate business and operational problems into measurable analytical hypotheses and production-ready analytical products.
- Strong ability to communicate complex analytical findings through visualizations and dashboards to both technical and non-technical users.
- Experience developing KPIs that reconcile to authoritative data sources.
- Understanding of modern lakehouse and Bronze / Silver / Gold architectures.
- Ability to work with Data Engineers and Architects to define data structures required for analytics and visualization.
- Experience developing auditable and explainable analytical methodologies appropriate for financial or regulated environments.
- Ability to operate within Agile product-development and iterative delivery environments.
- Ability to meet applicable DHA/DoD security, privacy, access, and data-handling requirements.
Preferred Qualifications - Prior experience with Advana and/or the current War Data Platform (WDP).
- Experience developing dashboards and analytical products within a DoD Databricks environment.
- Experience with Databricks Unity Catalog, Delta Lake, Databricks SQL, AI/BI Dashboards, MLflow, Workflows, or related capabilities.
- Healthcare revenue-cycle experience, including coding, claims, charge capture, denials, AR, remittance, payer reimbursement, and underpayment analysis.
- Familiarity with healthcare payer transaction data such as 835, 837, 270/271, 276/277, and 278 transactions.
- Experience with MHS GENESIS, Oracle Health/Cerner Millennium, Abacus, or similar healthcare systems.
- Experience supporting federal financial management, audit remediation, or revenue-recognition initiatives.
- Familiarity with certified data products, lineage, data governance, and data-quality controls.
- Experience developing explainable AI/ML capabilities in regulated or Government environments.
Target salary range: $140,375 - $185,604. Final compensation will be determined by a variety of factors including but not limited to your skills, experience, education, and/or certifications.
Job LocationsUS-Remote
