VP, AI Change & Portfolio Manager

The Bank of New York Mellon•New York, NY

58d•$81,000 - $130,000•Hybrid

About The Position

At BNY, our culture allows us to run our company better and enables employees' growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world's investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide. Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance - and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary. We're seeking a future team member for the role of VP AI Change & Portfolio Manager to join our Treasury Services team. This role is located in New York, NY- HYBRID The VP, AI Change & Portfolio Manager will be the architect of our organization's AI transformation-guiding end-to-end model delivery, driving adoption, and embedding governance to sustain long-term value. You'll bridge data science, engineering teams and business stakeholders to ensure AI solutions are designed, deployed, monitored and continuously improved, while championing change management practices that secure enterprise-wide engagement and accountability. In this role, you'll make an impact in the following ways:

Requirements

5+ years in change management, portfolio leadership or program management-plus 3+ years of hands-on experience building, deploying and maintaining AI/ML solutions in production.
Deep understanding of AI model development and MLOps practices (CI/CD for ML, model versioning, orchestration tools such as Kubeflow, MLflow, or equivalent).
Strong programming skills (Python, R or similar) and familiarity with cloud platforms (AWS, Azure, GCP) for AI workloads.
Demonstrated ability to establish governance frameworks around AI ethics, model risk and data privacy.
Exceptional communication and presentation skills-comfortable translating technical concepts for business audiences.
Proven track record designing and delivering training curricula and managing digital learning platforms (SharePoint, LMS).
Analytical mindset with experience defining, tracking and reporting on KPIs to drive accountability.
Collaborative approach, able to work across data science, engineering, operations and business functions.

Responsibilities

AI Model Lifecycle Management
Partner with data scientists and engineers to define requirements, develop, test and deploy production-grade AI/ML models.
Establish MLOps best practices-versioning, CI/CD pipelines, model serving and automated retraining workflows-to ensure reliability and scalability.
Monitor model performance in production (drift detection, performance metrics) and coordinate remediation or retraining to sustain accuracy and business impact.
Collaborate with IT and Cloud/Ops teams on infrastructure provisioning, security, and compliance for AI workloads.
Adoption and Accountability
Building Awareness: Develop multi-channel communications (roadshows, email campaigns, intranet microsites) to showcase AI solutions and ROI.
Empowering Through Education: Design interactive training programs (workshops, how-to guides, video tutorials) that cover both user-facing AI tools (e.g., Copilot) and the underlying model lifecycle.
Driving Adoption: Launch targeted adoption campaigns-kickoff workshops, hackathons, "AI Champions" network-and implement scorecards to hold teams accountable for integrating AI into their workflows.
Client & Stakeholder Support
On-going Support: Act as the central coordinator for troubleshooting AI model issues, tuning performance and scaling deployments.
Ad Hoc Expertise: Serve as the go-to adviser on model interpretability, data requirements, ethical considerations and use-case feasibility.
Governance & Change Management
Governance Framework: Define and operationalize AI governance policies (model risk management, data privacy, bias monitoring) in partnership with Legal, Risk and the AI Hub.
Change Advocacy: Embed structured change-control processes-change requests, impact assessments, steering-committee reviews-to maintain compliance and alignment.
Governance Forum & Portfolio Prioritization
Establish an AI Governance Forum: Convene a cross-functional steering committee-including representatives from Data Science, IT, Risk & Compliance, Legal, Finance and Business Units-to review, approve and oversee all AI initiative submissions.
Intake Process & Central Repository: Design a standardized proposal template and implement a centralized repository (SharePoint catalog or AI portfolio management tool) to capture initiative metadata-objectives, data requirements, resource estimates, risk ratings and expected ROI.
Prioritization Methodology: Develop a consistent scoring model and prioritization rubric based on strategic alignment, business value, technical feasibility, risk profile and compliance impact.
Quarterly Portfolio Reviews: Facilitate regular forum meetings to evaluate progress, re-prioritize initiatives, allocate resources and surface any governance or change-management risks.
Communication & Thought Leadership
Executive Liaison: Translate senior management's AI vision into clear roadmaps and deliverables; present status updates and impact analyses at leadership forums.
Fortnightly Newsletter & SharePoint: Curate content-use-case spotlights, metrics dashboards, upcoming milestones-and maintain a dynamic portal for all AI-related resources.
Material Creation & Training Delivery
Content Development: Author comprehensive training materials, including best-practice playbooks on model development and sustaining AI at scale.
Workshops & Webinars: Design and lead interactive sessions that walk stakeholders through real-world model deployment scenarios and operational concerns.
Metrics, KPIs & Reporting
Define Success Metrics: Partner with Finance and Analytics to set measurable targets for model adoption, uptime, performance improvement and cost savings.
Dashboards & Scorecards: Build and maintain reporting tools to track AI portfolio health and surface insights to senior leadership.