Senior Data Scientist (Fraud Detection and Investigative Analytics)

- Review, maintain, and extend all existing loan fraud indicators developed by TSD, and provide authoritative expertise on analytic method selection. - Design, develop, test, calibrate, and implement advanced statistical and machine learning models targeting financial fraud, improper payments, and non compliance within SBA programs. - Build and refine both supervised and unsupervised models, including regression, Bayesian, clustering, and ensemble approaches, and tune candidate models to determine best fit. - Perform data quality analysis on source tables to identify abnormalities and inconsistencies, and develop repeatable processes for combining and analyzing large relational, structured, and unstructured sources. - Collaborate directly with criminal investigators to determine and execute analytic strategies supporting loan fraud cases, adapting analysis as case needs shift and proactively surfacing data quality issues. - Adhere closely to the federal rules of criminal procedure governing protected information, including Rule 6(e). - Develop case leads for SBA OIG investigations from model outcomes. - Document all methodology, test models, and production models in a form that satisfies criminal evidentiary requirements. - Build visualizations and dashboards that convey methodological choices, outcomes, and predictive capability, and iterate them on end user feedback. - Deliver findings in multiple registers: data summaries and visualizations for investigative staff, executive summaries for OIG leadership. - Coordinate with the data engineering seat so the architecture supports machine learning efficiently. - Create programming and automation techniques that improve task efficiency using SharePoint, Python, Excel, Power BI, Power Apps, and similar tools. - Identify new business questions that expand the scope of analysis and reporting.

Master's, Ph.D., or doctorate level equivalent degree in data science, machine learning, computer science, mathematics, or a related field. Alternatively, ten years of applied work experience in any of the same fields.

- 5+ years Designing, implementing, and maintaining advanced AI systems and predictive models, including both supervised and unsupervised models. - 5+ years Developing analytic rules and models using leading edge analytic tools and best practices. - 5+ years Developing regression, classification, and other statistical models to identify anomalies, patterns, and predictive variables. - 3+ years Providing data support for criminal investigations into financial fraud or abuse of government funds. - 3+ years Manipulating data in Python.

Pandas is required. - 3+ years Working in a modern cloud environment: Azure, AWS, or GCP. Certifications preferred. - 2+ years Conducting advanced data analysis in SQL, specifically SQL Server and PostgreSQL. - 2+ years Developing and scaling natural language processing solutions. - 2+ years Presenting methods and findings to technical and non technical stakeholders, both orally and in written products and visualizations.

- Cloud certification in Azure, AWS, or GCP. - Direct experience with SBA loan programs, including 7(a), 504, EIDL, or PPP, or with comparable federal lending or grant fraud. - Entity resolution, record linkage, or graph and network analysis applied to fraud. - Experience producing analytic products that were used in a criminal referral or prosecution. - Model explainability practice such as SHAP or comparable feature attribution methods.