Senior Data Scientist (Fraud Detection and Investigative Analytics)
New
N
Node.DigitalFederal Government / Financial Fraud
Washington, District of Columbia, United States / Herndon, VA (Remote Work)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of relevant experience or equivalent degree.
- Required Skills
- AWSPostgreSQLPythonSQLGCPMachine LearningMicrosoft SQL ServerAzurePandasNLP
Requirements
- Master's, Ph.D., or doctorate in data science, machine learning, CS, math, or a related field; OR 10 years of applied experience.
- 5+ years of experience designing and implementing advanced AI systems and predictive models.
- 5+ years of experience developing analytic rules and models using leading-edge tools.
- 5+ years of experience in regression, classification, and statistical anomaly detection.
- 3+ years of experience providing data support for criminal investigations into financial fraud or abuse.
- 3+ years of experience manipulating data in Python (Pandas required).
- 3+ years of experience working in Azure, AWS, or GCP cloud environments.
- 2+ years of experience conducting advanced data analysis in SQL (SQL Server and PostgreSQL).
- 2+ years of experience developing and scaling natural language processing solutions.
- 2+ years of experience presenting complex findings to technical and non-technical stakeholders.
- Must have a Public Trust clearance.
Responsibilities
- Review, maintain, and extend existing loan fraud indicators while providing expertise on analytic method selection.
- Design, develop, test, and implement advanced statistical and machine learning models for fraud and improper payment detection.
- Perform data quality analysis to identify inconsistencies and develop repeatable processes for large datasets.
- Collaborate with criminal investigators to execute analytic strategies and adapt to shifting case needs.
- Build case leads for OIG investigations and document all methodologies to satisfy criminal evidentiary requirements.
- Create visualizations and dashboards to communicate findings to investigative and executive staff.
- Coordinate with data engineering teams to optimize architecture for machine learning.
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