Corporate Vice President - Model Validation and AI Governance

Location Designation: Hybrid - 3 days per week

Business Unit

Technology, Data, AI and Ventures (TDAV)

Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life’s businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes.

Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era — all backed by the stability and purpose of a mutual company built to last.

Role Overview

The Corporate Vice President – Model Validation and AI Governance will play a key leadership role in strengthening New York Life’s approach 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.

This role 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. A particular focus will be establishing robust approaches for evaluating agentic and multi-step AI systems, including tool use, orchestration, autonomy, guardrails, human oversight, observability, and emerging failure modes.

The successful candidate will combine deep quantitative and AI expertise with strong risk judgment and communication skills. They will establish validation standards and reusable practices, mentor junior colleagues, and communicate technical findings, limitations, and conditions of use in clear terms that enable senior stakeholders and governance forums to make informed decisions.

What You’ll Do

• 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 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. Assess statistical rigor, coverage, reproducibility, and the strength of validation evidence for high-impact use cases.
• 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, and partner 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; mentoring and reviewing the work of junior colleagues; partnering across data science, engineering, product, and control functions; and remaining current on evolving modeling techniques, AI research, evaluation methodolog…