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Technical Project Management, Senior Advisor
Peraton•United States•🌍 Remote
Full-time15+
$176k - $282k
per year
👁️ 0 views•📝 0 applications•Posted 10/5/2026•Expires 12/4/2026
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Job Description
Responsibilities Peraton is seeking a Technical Project Manager to fulfill the role of a Fraud Analytics SME to serve as the healthcare program-integrity subject-matter authority on an agentic AI proof-of-concept team supporting the Fraud Prevention System Program (FPS2) program for CMS, driving fraud/waste/abuse use-case selection, ground-truth curation, and analyst-facing adoption.
Working alongside AI engineers and investigators, the SME ensures AI-generated leads, dashboards, and case files reflect how Medicare and Medicaid fraud is actually detected, investigated, and adjudicated in the CMS program-integrity ecosystem.
Key Responsibilities
Serve as the healthcare program-integrity and FWA subject-matter authority on the AI tool assessment project within FPS. Drive FWA use-case selection for the POC, prioritized by fraud impact and analyst value.
Define and curate ground-truth datasets (known-bad-actor sets, adjudicated case outcomes, referral/enforcement outcomes) used to evaluate AI-generated leads and case files.
Shape analyst-facing use of Peraton [X] ( Peraton ’s AI tool) and Rapid FI (Fraud Intelligence) so generated leads, dashboards, and case files match real UPIC/MEDIC/MFCU/FDOC investigator workflows.
Evaluate AI-generated outputs for factual, clinical, and procedural accuracy; represent the analyst voice back to engineering. Partner with the Fraud Signals AI Engineer to validate Babel Street correlations against known fraud patterns, and with the Peraton [X] Support SME on user onboarding and training material.
Qualifications
Minimum of 12 years with BS/BA; Minimum of 10 years with MS/MA; Minimum of 7 years with Ph. D. 5+ years working with Medicare and/or Medicaid claims data in a program-integrity, audit, investigative, or fraud-analytics role.
Deep knowledge of healthcare FWA patterns: billing schemes, coding manipulation, identity/beneficiary fraud, provider collusion, DME/home-health/hospice/prescription typologies. Direct experience inside the CM
Required Skills
MedicareMedicaidfraud analyticsprogram integrityauditinvestigativebilling schemescoding manipulationidentity fraudbeneficiary fraudprovider collusionDMEhome healthhospiceprescription typologiesAIground-truth datasetscase outcomesreferral outcomesenforcement outcomesanalyst workflowsfactual accuracyclinical accuracyprocedural accuracyuser onboardingtraining material
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