Data & AI Product Strategy Leader- Commercial Banking

Description

Citizens is in the midst of a bold, enterprise-wide transformation—leveraging the power of data and AI to reimagine how we serve our customers, operate our business, and deliver value. This transformation spans multiple domains and requires a coordinated, strategic approach to integrating data across the organization.

As the Data & AI Product Strategy Leader for Commercial Banking you will lead the delivery of advanced data and intelligence products that empower commercial bankers and other clients facing colleagues with intelligence to prospect, engage, and deepen client relationships. This role is central to bridging cutting-edge AI capabilities ensuring every data product delivers measurable value to bankers and clients.

You will play a central role in executing this transformation strategy by owning and orchestrating the data components that fuel it. You’ll lead the horizontal enablement of data work streams, ensuring that product teams are aligned, supported, and delivering against strategic goals. This role is not just about data—it’s about driving enterprise outcomes through the thoughtful coordination of people, processes, and technology.

You’ll serve as a consultative partner to senior leaders, a problem solver for delivery teams, and a hands‑on product leader when needed. Your ability to navigate ambiguity, build relationships, and drive execution will be critical in bringing together diverse data efforts into a unified, impactful strategy.

The ideal candidate is a strategic thinker and hands‑on executor, able to lead without direct authority, manage conflicting priorities, and build strong partnerships across business, technology, and analytics teams. Success in this role requires a passion for innovation, a bias for action, and a proven track record of delivering results in highly matrixed, agile environments.

• Strategic Coordination & Program Enablement
  • Design and implement horizontal routines and governance structures that support cross‑functional collaboration.
  • Ensure strategic alignment between data product initiatives and enterprise transformation goals.
  • Serve as a liaison between product teams, program leadership, and business stakeholders to drive integrated planning and execution.
  • Champion a product mindset, focusing teams on solving customer problems and delivering value, not just executing requirements.
• Agile Delivery & Stakeholder Engagement
  • Operate within a complex “scrum of scrums” structure, coordinating within multiple agile pods and delivery teams to achieve program milestones.
  • Manage conflicting priorities and dependencies, ensuring transparency, accountability, and timely delivery.
  • Build and sustain strong partnerships with business, technology, and analytics stakeholders.
  • Communicate effectively across all levels of the organization, translating technical and strategic concepts into actionable insights.
• Budgeting & Financial Oversight
  • Support financial planning, budgeting, and resource allocation for data product initiatives.
  • Coordinate with delivery and domain leaders to develop and execute staffing plans that align with strategic priorities and program needs.
  • Act as a consultative partner to senior leaders and stakeholders, helping shape resource strategies and investment decisions through data‑driven insights and strategic framing.
• User‑Centric Product Design
  • Translate business requirements into actionable product roadmaps, prioritizing features that drive revenue, efficiency, and client satisfaction.
  • Lead the design and delivery of data products that provide bankers with a “single pane of glass” experience—centralizing insights, analytics, and next‑best‑action recommendations within business workflows.
  • Champion intuitive user experiences, ensuring complex AI outputs are accessible, explainable, and actionable for bankers.
• AI & Data Enablement
  • Guide the development of AI‑powered analytics engines for sales processes.
  • Partner with data engineering, architecture, ML Ops and technology teams to ensure guiding the build out of robust data infrastructure including business layer, data pipelines, data controls as well as relevant data needed for training…