About this opportunity
LMI Government Consulting lists this Senior Data Scientist - Clearance Required opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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
Theobjectiveis not simply to produce reports. The role will help create analytical products andDatabricks-based dashboards thatidentifywhere revenue-cycle processes are failing, quantifyfinancial 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 withinDatabricks using Python, SQL, Spark/PySpark, statistical methods, and machine-learning techniques.
DevelopDatabricks visualizations, Databricks SQL dashboards, AI/BI dashboards, and related native visualization capabilities to provide operational and executive visibility intoRevOSperformance.
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-levelRevOSSITREP 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 thatidentifyand quantify potentialrevenue leakage and recovery opportunities .
Analyze encounter, documentation, coding, charge, claim, denial, adjudication, payment, and accounts-receivable data toidentifypatterns 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
Developrecoverability and priority-scoring models based on financial value, probability of recovery, aging, filing/appeal deadlines, and operational severity.
Develop payer-performance and denial analytics toidentifyrecurring payer behaviors, denial patterns, reimbursement variances, and process failures.
Build predictive models thatidentifyrevenue-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 whilemaintainingseparation between analytical estimates and official accounting determinations.
Develop andvalidatestandardizedRevOSKPIs and analytical measures.
Support development of theRevOSRevenue 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 ensureSilver and Goldstructures support analytical, visualization, and dashboard performance requirements.
Optimizeanalytical 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 modelpurpose, features,methodology, validation, performance, refresh cadence, limitations, and version history.
Support model monitoring, validation, retraining, andModelOpspractices.
Qualifications
Required Qualifications
8+ years of professional experience in data science, advanced analytics, quantitative analysis, machine learning, or related disciplines.
Strong hands-on experience withDatabricks .
Demonstrated ability to useDatabricks 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.
Advancedproficiencywith Python and SQL.
Experience with Spark/PySparkor 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 modernlakehouseand Bronze / Silver / Gold architectures.
Ability to work with Data Engineers and Architects to define data structuresrequiredfor analytics and visualization.
Experience developing auditable and explainable analytical methodologiesappropriate forfinancial or regulated environments.
Ability tooperatewithin Agile product-development and iterative delivery environments.
Ability to meet applicable DHA/DoD security, privacy, access, and data-handling requirements.
Preferred Qualifications
Prior experience withAdvanaand/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,orrelated 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 federalfinancial 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.
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.
Job Locations
US-Remote
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.