Senior Data Scientist, NLP, Unstructured Data Analytics

Node.Digital

Senior Data Scientist managing NLP and unstructured data analytics for financial fraud detection. Collaborating with investigators and optimizing machine learning models within the federal framework.

Posted 7/30/2026full-timeRemote • District of Columbia, Washington • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and implementing advanced machine learning models and natural language processing solutions, with a strong focus on financial fraud detection and compliance. Proficient in data analysis, visualization, and collaboration with investigative teams to support criminal cases.

Highest-signal resume keywords

Natural Language ProcessingMachine Learning Model DevelopmentData Analysis in PythonCloud Environment ExperienceStatistical Modeling

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Hard Skills

Statistical ModelingMachine LearningNatural Language ProcessingData AnalysisOptical Character RecognitionSQLPythonRegression AnalysisClusteringBayesian Methods

Soft Skills

CollaborationCommunicationProblem-SolvingPresentation Skills

Tools & Technologies

Power BIPower AppsExcelAzureAWSGCPPandasSharePoint

Certifications & Qualifications

Cloud Certification in AzureCloud Certification in AWSCloud Certification in GCP

Industry Keywords

Financial FraudGovernment FundsCriminal InvestigationsData Quality AnalysisEvidentiary Requirements

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPandasPostgresPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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.
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

Comp & perks

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