Note: The job is a remote job and is open to candidates in USA. The Leading Niche is seeking a Technical Analytics Manager / Lead Data Scientist to serve as the senior technical lead for advanced fraud analytics solutions. This role involves leading analytic model development and collaborating with various stakeholders to detect and prevent fraud, waste, and abuse in federal benefit programs.
Responsibilities
- Lead the design and execution of advanced analytics projects supporting fraud detection, fraud prevention, and investigative intelligence
- Develop technical approaches for identifying fraud, waste, abuse, and mismanagement across large-scale federal benefit programs
- Serve as the senior technical advisor for analytic methodology, model design, fraud risk indicators, and analytic best practices
- Provide direction and oversight to data scientists, graph analysts, data engineers, investigative analysts, and other technical team members
- Translate complex fraud detection requirements into practical analytic approaches and measurable project plans
- Develop analytic rules, risk models, machine learning models, and fraud detection algorithms
- Design and implement use cases focused on identifying suspicious activity, fraud indicators, anomalous behavior, and emerging fraud schemes
- Apply advanced analytic techniques such as: Entity resolution, Data matching, Anomaly detection, Risk scoring, Predictive modeling, Graph analytics, Link analysis, Natural language processing, Machine learning, Artificial intelligence
- Support cross-program and cross-agency fraud analytics involving complex federal benefit datasets
- Manage model development, testing, validation, and refinement efforts
- Conduct quality control reviews of analytic outputs, models, code, assumptions, and methodologies
- Ensure analytic models are accurate, reliable, repeatable, and defensible
- Review contractor team work products before findings, models, and insights are finalized
- Identify model performance issues and guide recalibration or improvement activities
- Ensure all analytic work is thoroughly documented from data ingestion through final output
- Ideate, define, and prioritize innovative fraud detection and prevention use cases
- Collaborate with PRAC stakeholders, OIG partners, law enforcement users, and technical teams to select high-value analytic opportunities
- Identify fraud typologies, risk indicators, and patterns relevant to federal benefit programs
- Recommend new analytic methods, technologies, tools, and data sources to strengthen fraud detection capabilities
- Support continuous improvement of PRAC’s analytic methodologies and technical delivery processes
- Code analytic rules, models, and workflows using open-source programming languages and tools
- Work with large, complex, structured and unstructured datasets
- Collaborate with data engineering teams to ensure analytic models are supported by reliable pipelines, data quality controls, and governance processes
- Support integration of analytic models into cloud-based data environments and production workflows
- Develop reusable code, documentation, and technical artifacts to support long-term sustainment
- Work closely with PRAC leadership, OIG partners, law enforcement stakeholders, and internal technical teams
- Present technical concepts, model findings, analytic results, and recommendations to both technical and non-technical audiences
- Prepare written summaries, technical documentation, presentations, and briefings
- Communicate risks, issues, assumptions, dependencies, and recommended courses of action clearly and effectively
- Support project meetings, sprint planning sessions, technical reviews, and stakeholder demonstrations
- Track technical project progress, model development status, and analytic workstream milestones
- Identify risks, blockers, and issues that may affect project delivery, quality, or mission outcomes
- Support mitigation strategies to ensure projects are delivered on time and at a high level of quality
- Coordinate with the Project Manager to align analytic delivery with project schedules, deliverables, and Government expectations
Skills
- Minimum five (5) years of hands-on experience developing analytic rules and models for fraud detection use cases
- Minimum five (5) years of experience designing analytic approaches, managing model development and testing efforts, and conducting thorough quality control
- Minimum five (5) years of experience coding analytic rules and models using open-source programming languages and tools
- Minimum five (5) years of experience tracking technical project progress and identifying and mitigating project risks and issues
- Experience developing innovative fraud, waste, abuse, and mismanagement detection use cases
- Experience reviewing and quality checking analytic work products before models and insights are finalized
- Strong oral and written communication skills
- Fraud detection analytics
- Data science model development
- Machine learning
- Statistical analysis
- Risk modeling
- Anomaly detection
- Entity resolution
- Data matching
- Python or similar open-source programming languages
- Model testing and validation
- Analytic quality control
- Technical documentation
- Agile analytics delivery
- Experience supporting PRAC, CIGIE, Offices of Inspector General, federal law enforcement, or federal oversight organizations
- Experience supporting fraud analytics for federal benefit programs such as PPP, EIDL, RRF, SVOG, unemployment insurance, or similar large-scale relief programs
- Experience with graph analytics, network analysis, or link analysis
- Experience working with cloud-based analytics environments
- Familiarity with Azure Databricks, Microsoft SQL Server, Microsoft Power BI, Neo4j, or similar platforms
- Experience working with public, non-public, and commercially available datasets
- Experience supporting program integrity, investigative intelligence, or financial oversight missions
- Advanced degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Operations Research, Analytics, or a related field preferred
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