Senior Data Scientist

Role Description

Senior Data Scientist (NLP and Unstructured Data Analytics)

Location: Herndon, VA (Remote Work)

Must have a Public Trust Clearance

Key Responsibilities

• Integrate and scale natural language processing methods to parse, clean, and analyze large corpora of unstructured and semi-structured text, using optical character recognition, semantic similarity algorithms, and large language models as needed.
• Design, develop, test, calibrate, and implement statistical and machine learning models targeting financial fraud, improper payments, and non-compliance within SBA programs.
• Build and refine supervised and unsupervised models, including regression, Bayesian, clustering, and ensemble approaches.
• Review, maintain, and support all existing loan fraud indicators developed by TSD.
• Perform data quality analysis on source tables and develop repeatable processes for combining and analyzing large data sources.
• Collaborate directly with criminal investigators to determine and execute analytic strategies supporting loan fraud cases, adhering 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 conveying methodological choices, outcomes, and predictive capability, iterated 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 and text processing pipelines efficiently.
• Create programming and automation techniques using SharePoint, Python, Excel, Power BI, Power Apps, and similar tools.
• Identify new business questions that expand the scope of analysis and reporting.

Qualifications

• 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.

Preferred Qualifications

• Production experience with named entity recognition and entity resolution across messy document corpora.
• Retrieval augmented generation, vector stores, embeddings, and semantic search at scale.
• Large language model integration under federal security constraints, including boundary controlled deployment and prompt versioning.
• Optical character recognition pipelines applied to scanned or low quality source documents.
• Topic modeling, document classification, or clustering applied to audit, legal, or investigative text.
• Cloud certification in Azure, AWS, or GCP.

Benefits

• Medical
• Dental
• Vision
• Basic Life
• Health Saving Account
• 401K matching
• Three weeks of PTO/Sick
• 11 Paid Holidays
• Pre-Approved Online Training