Data Scientist

Dynamis; Washington (Washington County); On-site; USD 90,000 - 130,000; Full time

Job summary

Dynamis is seeking a Data Scientist to design and deploy ML models for detecting financial crime patterns using BSA/AML data. The role requires on-site work in Washington, DC, and an active TS clearance with SCI eligibility.

You will collaborate with compliance analysts, perform advanced data analyses, and document data pipelines and model logic to support investigative missions.

Qualifications

• U.S. Citizenship required.
• Top Secret clearance with eligibility for SCI required.
• Experience with statistical modeling and machine learning in Python and R.
• Hands-on experience with AWS cloud-native services (S3, RDS, OpenSearch, Lambda).

Responsibilities

• Design, develop, and deploy machine learning models and statistical algorithms to detect financial crime patterns using transactional 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 structured and unstructured data in AWS S3, PostgreSQL RDS, and OpenSearch.
• Collaborate with compliance analysts to translate regulatory requirements into data analyses and models.
• Produce visualizations and written findings for technical and non-technical stakeholders.
• Maintain documentation for data pipelines, model logic, and analyses in line with standards.
• Participate in peer code reviews and advance reproducible data science workflows.

Skills

Python

R

SQL

AWS

Education

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field

Tools

OpenSearch

AWS S3

RDS

Lambda

Job description

Position Title: Data Scientist

Dynamis is an Affirative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, age, marital status, pregnancy, genetic information, or other legally protected status.

If you'd like to view a copy of Dynamis' affirmative action plan, please e-mail [e-mail]. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact [e-mail]. This e-mail address is reserved solely for job seekers with disabilities requesting accessibility assistance or an accommodation in the job application process. Please do not call about the status of your job application if you do not require accessibility assistance or an accommodation. Messages sent for other purposes, such as following up on an application or non-disability related technical issues, will not receive a response.

Washington, DC

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 GIDs 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


• Bachelors degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field


• 45 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.