Data and AI Risk & Ethics Specialist

About this role

To qualify for a Data and AI Risk & Ethics Specialist, your resume and supporting documentation must support: A. Basic Requirements: Applicants must meet one of the following requirements: Degree: Mathematics, statistics, or actuarial science. 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 as shown in paragraph A above, plus additional education or appropriate experience.

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-12 level, applicants must possess one year of specialized experience equivalent to the GS-11 level or equivalent under other pay systems in the Federal service, military or private sector.

Applicants must meet eligibility requirements including minimum qualifications, and any other regulatory requirements by the cut-off/closing date of the announcement. Creditable specialized experience includes: Evaluating Artificial Intelligence and Machine Learning (AI/ML) systems to identify technical, operational, security, societal, organizational, and mission risks, including operational vulnerabilities and data-poisoning risks.

Applying the DoW AI Ethical Principles, DoW Unbiased AI Principles, Responsible AI Guidelines, Risk Management Framework (RMF), DoW Cloud Computing Security Requirements Guide, or comparable governance and risk-management standards to support responsible AI development and use. Collaborating with appropriate technical, legal, privacy, cybersecurity, and program personnel to assess and address data privacy, data security, and data reusability concerns involving Personally Identifiable Information (PII), Protected Health Information (PHI), or other sensitive data used in AI/ML solutions.

Preparing and communicating AI risk-assessment findings, mitigation strategies, governance guidance, and policy recommendations to organizational leaders, AI developers, program officials, DoW components, interagency partners, or other stakeholders. Reviewing and documenting AI design, development, acquisition, testing, deployment, or use activities to ensure identified risks, technical decisions, risk controls, system changes, and assurance efforts are properly documented and updated throughout the AI lifecycle.