Data Scientist

Omniscius Consulting•Washington, DC

27d•Onsite

About The Position

Our client is seeking an experienced Data Scientist to support a U.S. Department of the Treasury Financial Crimes Enforcement Network (FinCEN) program in Washington, DC. This position will apply advanced data science, statistical modeling, and machine learning techniques to large-scale financial datasets in support of financial crime detection and analysis. The ideal candidate will have strong hands-on experience with Python, R, machine learning, AWS cloud-native technologies, and large-scale data analysis. Experience working with Bank Secrecy Act (BSA), Anti-Money Laundering (AML), or related financial crime data is highly preferred.

Requirements

8+ years of overall professional experience.
4–5+ years of professional experience working as a Data Scientist.
Strong experience with statistical modeling and machine learning.
Hands-on programming experience with Python and R.
Experience with Python data science tools and frameworks, including Jupyter Notebook, PySpark, and Pandas.
Strong SQL skills with experience performing complex queries against large datasets.
Hands-on experience with AWS cloud-native services such as S3, RDS, OpenSearch, and Lambda.
Experience analyzing large-scale structured and unstructured datasets.
Working knowledge of Bank Secrecy Act (BSA) data.
Ability to translate business, regulatory, and investigative requirements into analytical approaches and models.
Experience producing technical documentation and communicating analytical findings to diverse audiences.
Bachelor's degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
Active Top Secret clearance with eligibility for access to Sensitive Compartmented Information (SCI).
U.S. Citizenship Required; No Dual Citizenship.
Interim clearances of any type will not be accepted.

Nice To Haves

Experience supporting FinCEN, the U.S. Department of the Treasury, or another Federal financial/regulatory organization.
Experience working with BSA/AML datasets.
Understanding of financial crime and Anti-Money Laundering methodologies.
Experience developing models designed to identify structuring, layering, smurfing, or other suspicious transaction patterns.
Experience collaborating directly with financial crime investigators, compliance analysts, or regulatory personnel.
Experience developing data science solutions within secure Federal environments.

Responsibilities

Design, develop, validate, and deploy machine learning models and statistical algorithms to identify financial crime patterns using BSA/AML transaction data.
Develop analytical approaches for detecting activities such as structuring, layering, smurfing, and other potentially suspicious financial behavior.
Perform exploratory data analysis, feature engineering, statistical analysis, and model validation.
Develop data science solutions using Python, Jupyter Notebook, PySpark, Pandas, R, and related technologies.
Use SQL to perform complex queries and analyze large-scale structured and unstructured datasets.
Work with data stored and processed through AWS services, including S3, PostgreSQL RDS, OpenSearch, Lambda, and related cloud-native technologies.
Collaborate with compliance analysts, investigators, and technical stakeholders to translate regulatory and investigative requirements into data analyses and analytical models.
Develop visualizations and communicate analytical findings to both technical and non-technical stakeholders.
Document data pipelines, analytical methodologies, model logic, assumptions, and findings in accordance with agency and organizational standards.
Participate in peer code reviews and contribute to best practices for reproducible, maintainable data science workflows.
Support continuous improvement of analytical models and methodologies used to identify financial crime risks and patterns.