AI Risk & Governance Lead (Bias, Safety, Fairness)

S&P Global Mobility; London; On-site; CAD 120,000 - 180,000; Full time

Job summary

S&P Global Mobility seeks an AI Risk Manager to maintain our AI risk taxonomy and assessment framework, developing rubrics for bias, fairness, explainability, drift tolerance, and harmful output while aligning with governance thresholds.

You will craft templates for model cards, risk assessments, and impact analyses, lead periodic independent audits, and collaborate with Engineering and Cybersecurity to ensure safety and reliability of AI deployments.

Qualifications

• Experience in AI/ML risk management or related analytical function
• Familiarity with risk assessment across safety, fairness, reliability, explainability
• Hands-on experience with model evaluation or bias testing is a plus

Responsibilities

• Maintain AI risk taxonomy and assessment methods across safety, fairness, reliability, explainability, robustness, and harmful output risks
• Develop rubrics for AI risk dimensions (bias testing, fairness metrics, explainability) within risk thresholds
• Create and maintain templates for risk documentation (model cards, risk assessments, impact analyses, risk scorecards)
• Support project teams in applying the risk assessment framework and quality gates, escalating high-risk deployments for direct review
• Execute periodic independent risk audits to verify federated execution meets standards
• Coordinate red-teaming and adversarial testing with Cybersecurity focusing on safety, bias, and harmful outputs

Skills

AI risk taxonomy maintenance

Developing risk evaluation rubrics

Risk documentation templates

Model evaluation & fairness testing

Audit execution & independent review

Cross-functional collaboration

Job description

S&P Global Mobility seeks an AI Risk Manager to maintain our AI risk taxonomy and assessment framework, developing rubrics for bias, fairness, explainability, drift tolerance, and harmful output while aligning with governance thresholds.

You will craft templates for model cards, risk assessments, and impact analyses, lead periodic independent audits, and collaborate with Engineering and Cybersecurity to ensure safety and reliability of AI deployments.