{"schemaVersion":"jobsearcher.job.v1","id":"697e0bcbc97b72c9e1d82e8b","url":"https://jobsearcher.com/jobs/697e0bcbc97b72c9e1d82e8b","canonicalUrl":"https://jobsearcher.com/jobs/697e0bcbc97b72c9e1d82e8b","title":"Data Scientist","description":"Job DescriptionDynamis 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 dataPerform exploratory data analysis, feature engineering, and model validation using Python, Jupyter Notebook, PySpark, Pandas, and RUse SQL for complex querying and analyze large-scale structured and unstructured datasets stored in AWS S3, PostgreSQL RDS, and OpenSearchWork with large data environments storing financial transactions or other critical data, including performing entity resolution across large datasetsUnderstand 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 8300sEnsure data quality and integrity through data mapping, cleaning, and validation processesApply quantitative and qualitative analysis techniques, statistical sampling, regression analysis, link analysis, geospatial analysis, social network analysis, and data mining, to financial dataCollaborate closely with compliance analysts and investigators to translate regulatory and investigative requirements into data analyses and analytical modelsProduce visualizations and written findings for both technical and non-technical stakeholders, as neededCommunicate 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 missionMaintain documentation for data pipelines, model logic, and analytical findings in accordance with agency or organizational standardsParticipate in peer code reviews and contribute to best practices for reproducible data science workflowsRequirements:U.S. CitizenshipBachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field4–5 years of work experience as a data scientist with strong knowledge of statistical modeling and machine learning experience using Python and RActive 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) dataDemonstrated experience with SQL for complex querying and analysis of large-scale structured and unstructured datasetsPreferred:Expertise in Python, Jupyter Notebook, R, NumPy, Pandas, and Scikit-LearnExperience with entity resolution across large, disparate financial datasetsExperience in research and delivery of analytic conclusions derived from financial data in support of investigative or compliance missionsPrior experience supporting a federal law enforcement, intelligence, or financial regulatory agency (e.g., FinCEN, ICE, DHS, Treasury)Salary range: $90,000-130,000The 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.","company":"Dynamis","rawCompany":"dynamis","city":"Andrews Air Force Base","state":"MD","isRemote":false,"isActive":false,"createdAt":"2026-09-04T04:01:27.567Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"33-3021.06","title":"Intelligence Analysts","slug":"intelligence-analysts"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"561611","title":"Investigation and Personal Background Check Services","slug":"investigation-and-personal-background-check-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Scientist","description":"Job DescriptionDynamis 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 dataPerform exploratory data analysis, feature engineering, and model validation using Python, Jupyter Notebook, PySpark, Pandas, and RUse SQL for complex querying and analyze large-scale structured and unstructured datasets stored in AWS S3, PostgreSQL RDS, and OpenSearchWork with large data environments storing financial transactions or other critical data, including performing entity resolution across large datasetsUnderstand 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 8300sEnsure data quality and integrity through data mapping, cleaning, and validation processesApply quantitative and qualitative analysis techniques, statistical sampling, regression analysis, link analysis, geospatial analysis, social network analysis, and data mining, to financial dataCollaborate closely with compliance analysts and investigators to translate regulatory and investigative requirements into data analyses and analytical modelsProduce visualizations and written findings for both technical and non-technical stakeholders, as neededCommunicate 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 missionMaintain documentation for data pipelines, model logic, and analytical findings in accordance with agency or organizational standardsParticipate in peer code reviews and contribute to best practices for reproducible data science workflowsRequirements:U.S. CitizenshipBachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field4–5 years of work experience as a data scientist with strong knowledge of statistical modeling and machine learning experience using Python and RActive 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) dataDemonstrated experience with SQL for complex querying and analysis of large-scale structured and unstructured datasetsPreferred:Expertise in Python, Jupyter Notebook, R, NumPy, Pandas, and Scikit-LearnExperience with entity resolution across large, disparate financial datasetsExperience in research and delivery of analytic conclusions derived from financial data in support of investigative or compliance missionsPrior experience supporting a federal law enforcement, intelligence, or financial regulatory agency (e.g., FinCEN, ICE, DHS, Treasury)Salary range: $90,000-130,000The 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.","datePosted":"2026-09-04T04:01:27.567Z","dateModified":"2026-09-04T04:01:27.567Z","hiringOrganization":{"@type":"Organization","name":"Dynamis","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Andrews Air Force Base","addressRegion":"MD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"697e0bcbc97b72c9e1d82e8b"},"url":"https://jobsearcher.com/jobs/697e0bcbc97b72c9e1d82e8b"}}