Data Scientist, Financial Crime & AML — TS/SCI

Dynamis, Inc.; Washington; On-site; USD 90,000 - 130,000; Full time

Job summary

Dynamis, Inc. 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 on-site work in Washington, DC, an active Top Secret clearance with SCI eligibility, and hands-on experience with AWS cloud-native services (S3, RDS, OpenSearch,

Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, CS, Engineering, or related field.
• 4–5 years of data science experience with ML and statistical modeling.
• Active Top-Secret clearance with SCI eligibility.
• Hands-on experience with AWS cloud services (S3, RDS, OpenSearch, Lambda).
• Working knowledge of BSA data and SQL for large-scale analyses.

Responsibilities

• Design, develop, and deploy ML models and statistical algorithms to detect financial crime patterns using BSA/AML data.
• Perform exploratory data analysis, feature engineering, and model validation with Python, Jupyter, PySpark, Pandas, and R.
• Use SQL for complex querying and analyze large-scale datasets stored in AWS S3, PostgreSQL RDS, and OpenSearch.
• Collaborate with compliance analysts and investigators to translate requirements into analytics and actionable findings.
• Produce visualizations and written findings for technical and non-technical stakeholders.
• Maintain documentation for data pipelines, model logic, and analytical findings according to standards.

Skills

Statistical modeling

Machine learning

Python

R

Data analysis

Education

Bachelor's degree in Data Science or related quantitative field

Tools

AWS

OpenSearch

S3

RDS

Lambda

SQL

PySpark

Jupyter Notebook

Job description

Dynamis, Inc. 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 on-site work in Washington, DC, an active Top Secret clearance with SCI eligibility, and hands-on experience with AWS cloud-native services (S3, RDS, OpenSearch,