Supervisory Data Scientist (AI/ML)

The division's independent scientific evaluation authority for all enterprise AI/ML capabilities by establishing standardized Test, Evaluation, Verification, and Validation protocols.

Directs the generation of Independent Validation and Verification reports that document critical statistical metrics such as ROC-AUC, F1-scores, and precision/recall distributions.

Oversees the branch's continuous monitoring protocols for models already in production, including defining mathematical drift thresholds and automated alerts, ensuring deployed models do not degrade or develop bias over time.

Collaborates with the J6DAA branch to ensure automated validation scripts are successfully integrated directly into the enterprise machine learning operations (MLOps) continuous Integration and continuous deployment pipelines.

Directs the branch's adversarial testing frameworks, such as data poisoning, model inversion, and evasion attacks, to proactively identify and mitigate vulnerabilities in AI systems.

To qualify for a Supervisory Data Scientist (AI/ML), your resume and supporting documentation must support: A. 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: Conducts formal AI risk assessments and provides mitigation strategies Certifies a model's scientific validity and readiness for the next phase of the deployment lifecycle, providing detailed test reports and statistical findings Mentors technical staff through highly complex statistical and ethical AI challenges 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.