Data Scientist (Supply Chain)

Performs professional work in data science, system analysis, risk analysis, predictive analysis, computer simulation, and program/project planning and controls.

Gathers, conditions, analyze, and integrate large and heterogenous volumes of data related to supply chain security risk assessments, for which little or no precedent exists, and where unconventional approaches are required in problem solving.

Conducts major data, system, and risk analyses on end-to-end supply chain, which includes item manufacturing and acquisition, storage and distribution, and disposition.

Uses scientific and statistical methods to solve novel organizational challenges and develop innovative, data-driven solutions.

To qualify for a Data Scientist (Supply Chain), your resume and supporting documentation must support: A. 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 1 above, plus additional education or appropriate experience. AND B.

Specialized Experience: One year of specialized experience that equipped you with the particular competencies to successfully perform the duties of the position and is directly in or related to this position.

To qualify at the GS-14 level, applicants must possess one year of specialized experience equivalent to the GS-13 level or equivalent under other pay systems in the Federal service, military or private sector.

Applicants must meet eligibility requirements including time-in-grade (General Schedule (GS) positions only), time-after-competitive appointment, minimum qualifications, and any other regulatory requirements by the cut-off/closing date of the announcement.

Creditable specialized experience includes: Applying scientific methods, to include, regression, decision tree analytics, probability and game theory, and statistical analysis to research and improve supply chain risk assessment processes.

Providing technical direction, planning, and leadership to create and implement new agency data and supply chain risk assessment policy and processes.

Providing a high level of technical expertise in data science, intelligence, artificial intelligence/machine learning (AI/ML), system analysis, risk analysis, predictive analysis, and computer simulation. Using standard-type models to simulate real world environments.