Data Scientist

Dynamis, Inc.•Washington, DC

14d•$90,000 - $130,000•Onsite

About The Position

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. 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).

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

Nice To Haves

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)

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