Data Scientist

Dynamis is seeking a Data Scientist to support FinCEN’s Global Investigations Division (GID). The practitioner will design, develop, and deploy machine learning models and statistical algorithms to detect financial crime patterns, such as structuring, layering, and smurfing, using BSA/AML transaction data. The role requires a strong understanding of statistical modeling and machine learning using Python and R, hands-on experience with AWS cloud-native services (S3, RDS, OpenSearch, Lambda), and working knowledge of Bank Secrecy Act (BSA) data, working in close collaboration with compliance analysts and investigators to turn regulatory and investigative requirements into analytical models and actionable findings.

Location: 1801 L Street NW, Washington, DC 20036. Position requires the ability to work on-site as required by FinCEN. Candidate must possess an active Top Secret clearance with eligibility for access to Sensitive Compartmented Information (SCI).

Responsibilities:

• Design, develop, and deploy machine learning models and statistical algorithms to detect financial crime patterns (e.g., structuring, layering, smurfing) using BSA/AML transaction data
• Perform exploratory data analysis, feature engineering, and model validation using Python, Jupyter Notebook, PySpark, Pandas, and R
• Use SQL for complex querying and analyze large-scale structured and unstructured datasets stored in AWS S3, PostgreSQL RDS, and OpenSearch
• Work with large data environments storing financial transactions or other critical data, including performing entity resolution across large datasets
• Understand the structure of bank wire transfer data, including international formats from message systems such as SWIFT, CHIPS, and book transfer systems, as well as BSA-derived data such as SARs, CTRs, and 8300s
• Ensure data quality and integrity through data mapping, cleaning, and validation processes
• Apply quantitative and qualitative analysis techniques, statistical sampling, regression analysis, link analysis, geospatial analysis, social network analysis, and data mining, to financial data
• Collaborate closely with compliance analysts and investigators to translate regulatory and investigative requirements into data analyses and analytical models
• Produce visualizations and written findings for both technical and non-technical stakeholders, as needed
• Communicate project progress, support needs, and analytical output to senior management, clearly conveying the “so what” and “why this matters” as it relates to GID’s mission
• Maintain documentation for data pipelines, model logic, and analytical findings in accordance with agency or organizational standards
• Participate in peer code reviews and contribute to best practices for reproducible data science workflows

Requirements:

• U.S. Citizenship
• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field
• 4–5 years of work experience as a data scientist with strong knowledge of statistical modeling and machine learning experience using Python and R
• Active Top-Secret clearance with eligibility for access to Sensitive Compartmented Information (SCI)
• Hands-on experience with AWS cloud-native services (e.g., S3, RDS, OpenSearch, Lambda)
• Working knowledge of Bank Secrecy Act (BSA) data
• Demonstrated experience with SQL for complex querying and analysis of large-scale structured and unstructured datasets

Preferred:

• Expertise in Python, Jupyter Notebook, R, NumPy, Pandas, and Scikit-Learn
  
• Experience with entity resolution across large, disparate financial datasets
  
• Experience in research and delivery of analytic conclusions derived from financial data in support of investigative or compliance missions
  
• Prior experience supporting a federal law enforcement, intelligence, or financial regulatory agency (e.g., FinCEN, ICE, DHS, Treasury)
  

Salary range: $90,000-130,000

The salary range for this position represents the anticipated hiring range. Actual compensation will be determined based on factors such as relevant experience, skills, education, certifications, and potential contract funding.