Senior Data Scientist (NLP and Unstructured Data Analytics)

Node.Digital•Washington, DC

6d•Remote

About The Position

This role involves integrating and scaling natural language processing (NLP) methods to parse, clean, and analyze large amounts of unstructured and semi-structured text. The position requires designing, developing, testing, and implementing statistical and machine learning models to target financial fraud, improper payments, and non-compliance within SBA programs. The Senior Data Scientist will build and refine supervised and unsupervised models, review and maintain existing fraud indicators, and perform data quality analysis. A key aspect of the role is collaborating directly with criminal investigators to develop analytic strategies for loan fraud cases, adhering to federal rules of criminal procedure. The position also involves developing case leads from model outcomes, documenting all methodology to meet evidentiary requirements, and creating visualizations and dashboards to communicate findings to various stakeholders. Coordination with data engineering for efficient pipelines and the creation of programming and automation techniques using various tools are also essential. The role encourages identifying new business questions to expand the scope of analysis and reporting.

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. 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.
Must have a Public Trust Clearance

Nice To Haves

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.

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, and 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 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.