Senior Data Scientist - Clearance Desired

Overview The 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 → Eligibility → Registration → Authorization → Patient Care → Documentation → Coding → Charge Capture → Claims → Adjudication → Remittance → Denials → Collections / Recovery   The 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.  LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors, helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. 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  Claims-readiness defects  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.  Qualifications Required 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 operationa