Senior Data Scientist (NLP and Unstructured Data Analytics)

About this role

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, 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.

Requirements

Required
Education
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+ yearsDesigning, implementing, and maintaining advanced AI systems and predictive models, including both supervised and unsupervised models.

5+ yearsDeveloping analytic rules and models using leading edge analytic tools and best practices.

5+ yearsDeveloping regression, classification, and other statistical models to identify anomalies, patterns, and predictive variables.

3+ yearsProviding data support for criminal investigations into financial fraud or abuse of government funds.

3+ yearsManipulating data in Python. Pandas is required.

3+ yearsWorking in a modern cloud environment: Azure, AWS, or GCP. Certifications preferred.

2+ yearsConducting advanced data analysis in SQL, specifically SQL Server and PostgreSQL.

2+ yearsDeveloping and scaling natural language processing solutions.

2+ yearsPresenting 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
We are proud to offer competitive compensation and benefits packages to include

Medical

Dental

Vision

Basic Life

Health Saving Account

401K matching

Three weeks of PTO/Sick

11 Paid Holidays

Pre-Approved Online Training

Originally posted on Himalayas