Note: The job is a remote job and is open to candidates in USA. LMI is a digital solutions provider that delivers innovative technology and mission-ready AI to federal agencies. The Senior Data Scientist will support the Defense Health Agency Revenue Cycle Operating System initiative by developing Databricks-based analytics, predictive models, dashboards, and decision-support products that identify revenue leakage and recovery opportunities. The role will collaborate with data engineers, revenue-cycle subject matter experts, DHA stakeholders, and product leadership to transform complex healthcare and financial data into actionable intelligence.
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
Skills
- 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
- Applicants must meet eligibility requirements for a U.S. Government security clearance
- Only US Citizens are eligible for a security clearance
- For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work
- 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
Benefits
- US-Remote
Company Overview