USA remote
Supervisory Industry Economist (Associate Division Chief)
About this role
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. Current Federal employees must meet time-in-grade requirements by the closing date of this announcement.
GS-15 In order to be deemed as qualified candidate's resumes must demonstrate the required specialized experience in addition to the required competencies listed below. In addition, candidates must meet the educational qualification requirement. Education-Degree: economics, that included at least 21 semester hours in economics and 3 semester hours in statistics, accounting, or calculus OR a combination of education and experience: courses equivalent to a major in economics, as shown in A above, plus appropriate experience or additional education.
AND Specialized Experience Applicants must have a minimum of one year of specialized experience equivalent to at least the GS-14 the Federal service. For this position, specialized experience includes the following: 1. Overseeing the design, implementation, or improvement of advanced data analytics involving large or nationwide datasets, complex geospatial data, and/or crowd-sourced technical datasets requiring sophisticated data cleansing techniques; 2.
Establishing or implementing data quality, data validation, data management, or analytical standards and processes across multiple programs or organizational components; 3. Providing technical leadership in the use of advanced statistical, analytical, computational, or data science methods to evaluate data quality, solve complex problems, identify trends, and support organizational or policy decisions; 4. 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 5.