The engineer performs secure code and model handling, conducts performance tuning, automates secure build and deployment pipelines, and coordinates with security/SSO personnel to prevent inadvertent disclosure during agile prototyping. The position advances cutting‑edge AI capabilities by designing and evaluating new analytic methods, conducting independent research, and incorporating emerging technologies to enhance all‑source intelligence production, knowledge management, and analytic tradecraft. The Data Scientist leverages advanced statistical methods, develops semantic automation workflows, improves knowledge‑graph reasoning, enhances entity extraction and summarization, and conducts multi‑INT analytic augmentation through ML.
This role also supports tool and workflow modernization, performs data‑science scripting in R/Python/SQL, manages disparate data sources, and evaluates AI/ML techniques for risks, biases, and limitations. The Data Scientist contributes to incremental capability enhancements, maintains familiarity with state‑of‑the‑art technologies, and produces analytic outputs on emerging military technologies worldwide.
Responsibilities
Design, implement, and secure production ML systems and deployments for classified environments, including hardened architectures, microservices, containerized/clustered deployments, RMF/OPSEC compliance, and integration with mission systems.
Conduct secure code and model handling, performance tuning, and automation of secure CI/CD build and deployment pipelines; coordinate with SSO/security to prevent inadvertent disclosure during development.
Design, develop, and evaluate leading‑edge AI technologies for integration into all‑source analysis tradecraft, assessments, production workflows, dissemination, and knowledge management.
Propose and apply advanced statistical and mathematical techniques to support model formulation, experimental design, and development of new analytic methodologies.
Collaborate with team members on exploratory efforts using large language models, NLP, and ML to automate ontologies and ensure semantic accuracy and interoperability with knowledge‑graph architectures.
Evaluate AI, data‑science, and advanced analytic methods for risks, biases, and limitations that may distort analytic conclusions.
Develop and refine semantic data‑retrieval and reasoning capabilities across knowledge graphs using GraphQL, SPARQL, SHACL, SQL, and other query frameworks.
