Job Requirements
Washington, DC
Top Secret/SCI Polygraph not specified
Mid Level Career (5+ yrs experience)
$150,000 - $190,000
Job Description
In this role, you will apply data science and machine learning to analyze complex financial transaction data for a major federal intelligence and law enforcement bureau. Your analytical models will help identify suspicious patterns, supporting efforts to safeguard the financial system from illicit activity and money laundering. This work would be performed on-site in Washington, DC.
Key Responsibilities:
Financial Crime Pattern Detection: Designing, developing, and deploying machine learning models and statistical algorithms to identify complex money laundering techniques—such as structuring, layering, and smurfing—within massive financial datasets.
Cloud-Native Data Engineering & Analysis: Performing exploratory data analysis, feature engineering, and model validation using Python, PySpark, and SQL across scalable AWS infrastructure, including S3, RDS, and OpenSearch.
Stakeholder Collaboration & Translation: Partnering directly with compliance analysts and federal investigators to translate complex regulatory requirements into high-impact analytical models and clear visual reports.
Model Integrity & Workflow Standardization: Building maintainable data pipelines, document model logic to agency standards, and lead peer code reviews to ensure reproducible, high-quality data science practices.
Required Qualifications:
Active Top Secret SCI clearance
Bachelor’s Degree or higher in a related field from an accredited college or university
4+ years of dedicated data science experience building, validating, and deploying machine learning models using Python and/or R.
Hands-on experience analyzing Bank Secrecy Act (BSA) or Anti-Money Laundering (AML) transaction data to detect illicit financial patterns.
Hands-on experience executing queries and managing data pipelines within AWS cloud environments (e.g., S3, RDS/PostgreSQL, OpenSearch, Lambda).
Strong proficiency with SQL and big-data frameworks (e.g., PySpark, Pandas) to analyze large-scale structured and unstructured datasets.
Preferred Qualifications:
Experience building intuitive data dashboards and clear visual reports to present findings to non-technical operational teams.
Demonstrated track record of establishing automated model documentation and reproducible data pipelines in a regulated environment.
Key Responsibilities:
Financial Crime Pattern Detection: Designing, developing, and deploying machine learning models and statistical algorithms to identify complex money laundering techniques—such as structuring, layering, and smurfing—within massive financial datasets.
Cloud-Native Data Engineering & Analysis: Performing exploratory data analysis, feature engineering, and model validation using Python, PySpark, and SQL across scalable AWS infrastructure, including S3, RDS, and OpenSearch.
Stakeholder Collaboration & Translation: Partnering directly with compliance analysts and federal investigators to translate complex regulatory requirements into high-impact analytical models and clear visual reports.
Model Integrity & Workflow Standardization: Building maintainable data pipelines, document model logic to agency standards, and lead peer code reviews to ensure reproducible, high-quality data science practices.
Required Qualifications:
Active Top Secret SCI clearance
Bachelor’s Degree or higher in a related field from an accredited college or university
4+ years of dedicated data science experience building, validating, and deploying machine learning models using Python and/or R.
Hands-on experience analyzing Bank Secrecy Act (BSA) or Anti-Money Laundering (AML) transaction data to detect illicit financial patterns.
Hands-on experience executing queries and managing data pipelines within AWS cloud environments (e.g., S3, RDS/PostgreSQL, OpenSearch, Lambda).
Strong proficiency with SQL and big-data frameworks (e.g., PySpark, Pandas) to analyze large-scale structured and unstructured datasets.
Preferred Qualifications:
Experience building intuitive data dashboards and clear visual reports to present findings to non-technical operational teams.
Demonstrated track record of establishing automated model documentation and reproducible data pipelines in a regulated environment.
group id: 91173093