SIU & Payment Integrity AI Analyst

Codoxo•Duluth, GA

3d•Hybrid

About The Position

Of the $3.8T we spend on healthcare in the United States annually, about a third of it is estimated to be lost due to waste, fraud and abuse. Codoxo is the premier provider of artificial intelligence-driven solutions and services that help healthcare companies and agencies proactively detect and reduce risks from fraud, waste, and abuse and ensure payment integrity. Codoxo helps clients manage costs across network management, clinical care, provider coding and billing, payment integrity, and special investigation units. Our software-as-a service applications are built on our proven Forensic AI Engine, which uses patented AI-based technology to identify problems and suspicious behavior far faster and earlier than traditional techniques. We are venture backed by some of the top investors in the country, with strong financials, and remain one of the fastest growing healthcare AI companies in the industry. Position Summary: Codoxo is seeking a [temporary] full-time Payment Integrity Data Curator to support the development and refinement of Codoxo’s AI-driven payment integrity products by expanding and curating high-quality datasets used to measure and improve AI performance. This is a temporary 6-month role. This is a specialized role designed for experienced payment integrity professionals who can translate operational investigation and audit expertise into structured datasets that support the next generation of healthcare AI tools. The PI Data Curator will work closely with Codoxo’s AI, product, and customer success teams to curate datasets that represent real-world payment integrity scenarios and investigative workflows across medical and pharmacy domains.

Requirements

CPC (Certified Professional Coder), CFE, AHFI or equivalent credential required.
Minimum 5 years of experience in SIU/investigations, healthcare payment integrity, and auditing.
Must have expertise in core coding and billing, fraud waste and abuse, and 2+ of the three areas below: Professional Claims, Facility Claims, Experience reviewing medical records for coding discrepancies.
Proven track record in identifying overpayments, waste, and abuse through claims analytics or audit experience; preferably in a leading or consulting role.
Strong understanding of payer systems, coding, and reimbursement methodologies (CPT, HCPCS, ICD-10) specific to each Medicare, Medicaid, and commercial payer policies and claims systems.
Ability to communicate complex findings clearly and succinctly to both technical and non-technical teams.
Comfortable providing clear feedback and validation on an ongoing, project-based schedule in a fast-paced environment.

Nice To Haves

High-value differentiators: DRG/APR-DRG auditing (MS-DRG/APR-DRG assignment validation, upcoding/DRG creep, LOS & medical necessity review); experience with medical necessity triggers, high-cost drug/services, pharmacy & DME integrity, behavioral health and Psychotherapy coding.
proven innovation record and systems-approach mindset, demonstrating ability to identify and drive process or product improvements.

Responsibilities

Review historical payment integrity/SIU cases and claims data to expand Codoxo’s curated ground truth datasets.
Identify and document confirmed fraud, waste, abuse, and payment error scenarios.
Annotate claims and investigation outcomes to support AI model training and benchmarking.
Ensure curated cases reflect real-world investigative findings and payer workflows.
Review outputs generated by Codoxo’s GenAI tools when analyzing medical records and investigation artifacts.
Provide structured feedback on whether generated insights reflect meaningful investigator workflows.
Review synthetic datasets created by Codoxo AI and data engineering teams.
Validate the realism of synthetic claims data, medical records, and diagnostic images.
Contribute to internal documentation and dataset curation guidelines that support ongoing AI product development.
Proactively identify new areas where data analysis can maximize product improvements with minimal effort, and maximal positive impact