Improper Payments Manager (Accounting) (160-257)

Location: Alexandria, VA (Hybrid – Onsite Mon–Thurs, Remote Fridays) Work Authorization: Must be a U.S. Citizen or Green Card holder Schedule: Full-time (40 hours/week) Compensation: Salary is open and commensurate with experience Overview We are seeking an experienced Improper Payments Manager to lead the design and implementation of a scalable improper payments program. This role focuses on identifying, preventing, and reducing payment errors and risks through analytics, process improvement, and internal controls across accounts payable operations. Key Responsibilities Design and implement a comprehensive improper payments program with a risk-based approach Analyze accounts payable data to detect duplicate, inaccurate, or unauthorized payments Conduct stakeholder interviews and document end-to-end payment processes and controls Perform gap assessments and evaluate internal controls (preventative vs. detective) Develop risk assessment frameworks and classify payment risks (low/medium/high) Build analytics and testing routines to identify anomalies and payment irregularities Develop statistical sampling methodologies for payment reviews and audit support Identify root causes of improper payments and recommend corrective actions Create dashboards, reports, and metrics to monitor program effectiveness Support quarterly reviews and ensure repeatable, scalable processes Lead documentation, training, and knowledge transfer to internal teams Qualifications Bachelor's degree in Accounting, Finance, or related field Active CPA required Minimum 5+ years of relevant experience , including 2+ years at a manager level Strong background in Accounts Payable processes and internal controls Experience developing or implementing improper payment or audit programs Advanced data analysis, querying, and reporting skills Experience with ERP systems and understanding of data structures Knowledge of risk assessment, compliance, and fraud detection methodologies Nice to Have Experience with predictive analytics or statistical sampling techniques Public sector or large enterprise environment experience J-18808-Ljbffr