Supervisory Data Scientist; Associate Division Chief

This position is located in the Office of Economics and Analytics (OEA), Data Division (DD), Federal Communications Commission (FCC), located in Washington, DC.

RELOCATION EXPENSES WILL NOT BE PAID.

THIS VACANCY ANNOUNCEMENT MAY BE USED TO FILL ADDITIONAL POSITIONS WITHIN 90 DAYS.

Interested candidates should be passionate about the ideals of our American republic, committed to upholding the rule of law and the U.S. Constitution, and committed to improving the efficiency of the Federal government. Hiring decisions will not be based on race, sex, color, religion, or national origin.

Applicants must meet eligibility and qualification requirements by the closing date of this announcement.
Time in grade restrictions do not apply to Direct Hire procedures.

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) plus additional education or appropriate experience.

1. Leading or overseeing complex, organization-wide data management and/or data governance programs, including the implementation, development, and/or operation of enterprise data catalogues, data warehouses, data lakes, or other data platforms;

2. Overseeing the design, development, implementation, or improvement of enterprise data platforms, large-scale data collections, analytical systems, dashboards, data models, or automated reporting solutions;

3. Managing the development and implementation of artificial intelligence (AI) or machine learning (ML), including through the use of Large Language Models (LLMs), to facilitate data discovery, enhance data analytics, and/or support data science programs with large, complex datasets;

4. Establishing or implementing data governance, data quality, validation, data management, or analytical standards and processes across multiple programs or organizational components;

5. Providing technical leadership in the use of advanced statistical, analytical, computational, or data science methods to identify trends, evaluate data quality, solve complex problems, and support organizational or policy decisions;

6. Coordinating and directing the implementation of data architecture, data frameworks, and technological infrastructure supporting data collections and complex datasets, including those containing large-scale or nationwide geospatial data;

7. Advising senior management on complex data, analytical, technical, or program issues and translating technical findings into clear recommendations for technical and non-technical audiences; and

8. Leading multidisciplinary teams and projects involving data scientists, analysts, information technology professionals, project managers, contractors, or other technical and professional staff.

PART-TIME OR UNPAID

EXPERIENCE:

Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, Ameri Corps) 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.

• Ability to apply advanced data science, statistical, computational, geospatial, and analytical methods to large, complex, structured, and unstructured datasets to identify patterns, evaluate data quality, solve complex problems, and develop reliable findings that support organizational and policy decisions.
• Skill leading the development and implementation of enterprise data strategies, governance frameworks, data standards, data quality controls, large-scale data collections, and data platforms to ensure that organizational data are accurate, accessible, secure, usable, and appropriately managed.
• Skill leading complex data programs, projects, and multidisciplinary teams involving data scientists, analysts, IT professionals, contractors, and other stakeholders.
• Ability to effectively establish…