CVP, Model Validation & AI Governance

Job Title: Corporate Vice President – Model Validation and AI Governance

Location: New York, NY (hybrid 3 days in office)

Job Summary The Corporate Vice President – Model Validation and AI Governance will play a key leadership role in strengthening enterprise approaches to model validation and responsible AI governance across predictive, generative, and agentic AI solutions. Working closely with Model Risk Management and partners across Artificial Intelligence & Data, Technology, Risk, Legal, Compliance, Cybersecurity, and Third-Party Risk Management, this role will translate model risk requirements into rigorous, practical validation approaches and controls.

The successful candidate will lead complex validation engagements end-to-end for internally developed and third-party solutions, independently challenging model methodologies, evaluation approaches, controls, and monitoring strategies, with a particular focus on establishing robust approaches for evaluating agentic and multi-step AI systems.

Key Responsibilities

• Lead independent validation and effective challenge across predictive, machine learning, generative AI, and agentic AI solutions, assessing model design, data and feature pipelines, methodologies, evaluation metrics, assumptions, limitations, and business impact.
• For agentic AI systems, evaluate architecture, planning and orchestration, tool use and permissions, retrieval and prompt design, memory and state, autonomy boundaries, guardrails, human-in-the-loop controls, and escalation and fallback mechanisms.
• Design and advance rigorous AI evaluation practices by developing and challenging evaluation frameworks, benchmark and golden datasets, rubric-based and LLM-as-a-judge scoring, human review protocols, regression suites, offline and online testing, and adversarial or red-team evaluations.
• Establish monitoring, observability, and third-party validation approaches that address model drift, bias and fairness, stability, hallucinations, business outcomes, and agentic failure modes.
• Evaluate vendor and foundation-model solutions through independent testing and due diligence, partnering with teams to establish tracing, logging, telemetry, dashboards, alerts, compensating controls, and audit-ready evidence.
• Translate risk and regulatory expectations into practical controls by interpreting technical standards, regulatory guidance, and internal procedures and converting requirements into validation methodologies, checklists, playbooks, standard operating procedures, monitoring expectations, and evidence requirements.
• Present validation findings, limitations, and conditions of use to senior stakeholders and governance committees.
• Set standards and strengthen validation capabilities across teams by developing reusable templates, guidelines, test harnesses, and best practices for predictive, generative, and agentic AI.
• Mentor and review the work of junior colleagues and partner across data science, engineering, product, and control functions.
• Remain current on evolving modeling techniques, AI research, evaluation methodologies, observability technologies, and governance practices.

Requirements

• Advanced degree in Statistics, Computer Science, Data Science, Mathematics, Economics, Engineering, or a related quantitative discipline.
• 7+ years of experience in model validation, model governance, or model risk management for predictive and AI/ML solutions within regulated environments.
• Deep hands-on knowledge of traditional statistical and machine learning approaches, combined with demonstrated experience validating generative and agentic AI systems, including multi-step workflows, tool use, orchestration and planning, guardrails, autonomy controls, and human oversight.
• Demonstrated experience designing, building, or independently reviewing AI evaluation frameworks, including dataset curation, benchmark design, rubric-based and LLM-as-a-judge evaluation, human review, regression testing, retrieval-quality assessment, and adversarial or red-team testing.
• Practical experience with AI/ML monitoring and observability, including tracing, logging, telemetry, dashboards, and alerting.
• Experience validating or overseeing third-party, vendor-hosted, foundation-model, or other limited-transparency AI solutions.
• Proficiency in Python and SQL, with experience using agent orchestration, evaluation, observability, or related AI tooling.
• Strong commun…