Data Scientist (AI)

In this role as a Data Scientist, you will be responsible for: Serving as a subject matter expert in the science of artificial intelligence and machine learning, advising and/or consulting with both internal and external stakeholders on issues concerning application of those technologies as needed.

Communicating complex technical concepts to non-technical stakeholders. Collaborating with other technical teams to integrate research and development outputs into production environments. Managing and guiding the technical development of projects by contracted developers.

Designing, developing, maintaining, and modifying quantitative models, metrics, and reports that support various program areas. Conducting quantitative and qualitative analyses and evaluations using a wide variety of commercial and SEC databases to support the mission of the SEC.

Developing and operationalizing AI models programmatically, including generating, parsing, and interpreting structured AI responses. Engineering high-performing prompts and retrieval-augmented workflows to refine accuracy and reduce errors. Designing agentic workflows to solve challenging tasks.

Implementing responsible AI practices to ensure outputs are reliable and aligned with organizational risk objectives. Writing clear, maintainable, well-documented code that supports long-term readability and team collaboration. Familiar with version control practices to ensure transparency, traceability, and collaborative development.

Designing analyses and experimenting to be fully reproducible through structured project organization, environment management, pinned dependencies, and deterministic workflows.

Applicants are responsible for confirming all required materials are submitted by the closing date of the announcement. Please check the How You Will Be Evaluated and Required Documents sections carefully, as missing documents will render the application incomplete and ineligible for review.

Qualifying experience may be obtained in the private or public sector. Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, AmeriCorps) and other organizations (e.g., professional, philanthropic, religious, spiritual, community, student, social).

Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience. All qualification requirements must be met by the closing date of this announcement.

BASIC REQUIREMENT: Degree: Mathematics, statistics, computer science, data science or field directly related to the position.

The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position. or Combination of education and experience: Courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience.

MINIMUM QUALIFICATION REQUIREMENT: In addition to meeting the basic requirement, applicants must also meet the minimum qualification requirement. SK-14: Applicant must have at least one year of specialized experience equivalent to the GS/SK-13 level: 1.

Implementing and operationalizing advanced artificial intelligence, and/or network analysis on datasets using the Python (or comparable) programming language; AND 2. Leading and overseeing the implementation of advanced intelligence projects.

ACCOMPLISHMENT RECORD COMPETENCIES: Your Accomplishment Record narratives should address the following competencies.

See the How You Will Be Evaluated section below for more information: Competency 1: Technical Expertise in AI/ML: Applies AI/ML, quantitative modeling, and statistical analysis skills to build and maintain models and advance research while applying responsible AI practices. Operationalizes AI systems into production.

Competency 2: Technical Execution: Produces clean, well-documented code. Uses reproducible workflows through strong version control, structured processes, and consistent attention to technical quality.

Competency 3: Technical Communication: Translates technical information into non-technical terms and accurately convey technical information to end users (e.g., staff, management) and outside parties, including the technical documentation of applications, systems, Standard Operating Procedures, etc.

Competency 4: Teamwork and Collaboration: Interacts with internal and external others in a manner that advances SEC goals and objectives.