DATA SCIENTIST (AI)

The Division of Economic and Risk Analysis is seeking a Data Scientist (AI), in the Office of Structured Disclosure. As a Data Scientist (AI), you will help maintain operational continuity, supports modernization initiatives, and strengthens the Commission's ability to provide investors, regulators, and the public with consistent and accessible machine-readable data.

**Qualifications:**
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: Basic Requirements: 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: Specialized experience includes all of the following: 1. Providing leadership on financial data, data standards, machine-readable languages, and analytical initiatives requiring advanced datatypes and multisource integration, and identifies and communicates emerging AI/ML tools and methods to strengthen organizational analytical capabilities. 2. Applying AI tools, including code assist technologies, automation, and AI-enabled analytical methods, to strengthen evaluation of machine-readable data, improve workflow efficiency, and support modernization efforts, while acting as a recognized technical resource for these applications. 3. Leading the development and enhancement of cloud and hybrid infrastructure to support analytical processing, data ingestion pipelines, structured data validation, and modernization activities across multilayer storage architectures, diverse databases, and cross platform migration and transformation workloads. 4. Supporting long-range planning, strategic decision making, and policy interpretation for structured data, analytics, cloud systems, and modernization efforts, and communicating analytical findings to leadership, program staff, and external stakeholders. ACCOMPLISHMENT RECORD COMPETENCIES: Your Accomplishment Record narratives should address the following competencies. See the How You Will Be Evaluated section below for more information: AI/ML Application & Responsible Use: Uses appropriate AI/ML methods and tools-including code assist and information processing automation-to enhance analysis of structured data, increase workflow efficiency, and modernize analytical or review processes while following responsible AI practices. Technology Awareness and Innovation: Maintains awareness of emerging analytics and AI/ML technologies and uses this knowledge to introduce and share relevant advancements that strengthen organizational capabilities. Teamwork and Collaboration: Interacts with internal and external others in a manner that advances SEC goals and objectives. Technical Quality and Reproducibility - Applies structured engineering practices and quality control techniques and methods across the development lifecycle, producing well documented code, maintaining version controlled artifacts, and creating reproducible workflows.