Senior Data Scientist (Fraud Detection and Investigative Analytics)

Node.Digital•Washington, DC

7d•Remote

About The Position

The Senior Data Scientist will focus on Fraud Detection and Investigative Analytics. This role involves reviewing and extending existing fraud indicators, designing and implementing advanced statistical and machine learning models for financial fraud, improper payments, and non-compliance within SBA programs. The position requires collaboration with criminal investigators, adherence to federal rules, and development of case leads. The role also includes creating visualizations, dashboards, and automation techniques to improve efficiency and identify new business questions. A Public Trust Clearance is required.

Requirements

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.
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.
Must have an Public Trust Clearance

Nice To Haves

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.

Responsibilities

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.