You lead by building: teams, products, and the strategy that connects them. Join us to shape how the bank turns customer data into intelligence people can act on, and to grow the product leaders who make it real. As a Product Director in Personalization & Customer Insights (Intelligence) team, you own the product vision and strategy for a portfolio of AI-powered intelligence products, and you lead the product managers who deliver it. You set direction, develop your team, and stay close enough to the work to pressure-test model design, evaluation, and experiment tradeoffs yourself. You act as the voice of the customer, translate research and analytics into a clear multi-year plan, and drive execution across a highly matrixed organization, aligning Data Scientists, ML Engineers, partner teams, and business stakeholders around a shared roadmap. You are accountable for what the team ships, on time and at quality, not just for setting direction. You own the products that give a clear, 1:1 understanding of each customer, built for customers and for the bankers who serve them. You make sure that intelligence is tested and validated before it reaches customers, and you drive the personalization strategy with the lines of business. You partner closely with the platform and modeling teams that build and serve the models, but your portfolio is the intelligence itself and how the business puts it to work. At its core, this work is about personalization that makes every interaction feel built around the individual: the sense that the bank knows them and is working for them. Delivering that at scale takes more than one great product; it takes a product organization with a clear strategy and the leadership to execute it. This role sets that vision, builds the team that delivers it, and drives it to reality across the organization. Job responsibilities 34; Oversees the product roadmap, vision, development, execution, risk management, and business growth targets 34; Leads the entire product life cycle through planning, execution, and future development by continuously adapting, developing new products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth 34; Coaches and mentors the product team on best practices, such as solution generation, market research, storyboarding, mind-mapping, prototyping methods, product adoption strategies, and product delivery, enabling them to effectively deliver on objectives 34; Owns product performance and is accountable for investing in enhancements to achieve business objectives 34; Monitors market trends, conducts competitive analysis, and identifies opportunities for product differentiation 34; Stay hands-on across the model lifecycle with Data Scientists and ML Engineers: model design, evaluation, and iteration for LLM/ML-based solutions. 34; Frame hypotheses and run experiments (e.g., A/B testing) to validate impact and measure outcomes, and raise the experimentation bar across your team. 34; Define and track product KPIs for engagement, quality, cost, risk posture, reliability, and business outcomes. 34; Align with partner teams on contracts, SLAs, and guardrails so intelligence is delivered safely and consistently across surfaces. 34; Be accountable for responsible AI and data practices across your portfolio, including consent management and fairness, in partnership with Risk, Privacy, and Compliance. 34; Drive execution across a highly matrixed organization and represent the Intelligence space to executive stakeholders. Required qualifications, capabilities, and skills 34; 8+ years of experience or equivalent expertise delivering products, projects, or technology applications 34; Extensive knowledge of the product development life cycle, technical design, and data analytics 34; Proven ability to influence the adoption of key product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management 34; Experience driving change within organizations and managing stakeholders across multiple functions 34; Proven success delivering AI/ML-powered products to production in customer-facing environments, in close partnership with Data Science and ML Engineering across the model lifecycle. 34; Demonstrated success with ML-driven personalization and/or next-best-action, including experimentation and rigorous outcome measurement. 34; Strong data literacy, able to turn research and metrics into decisions and roadmaps that deliver on time, cost, and quality. 34; Hands-on data fluency: able to explore and query data yourself (SQL/Python, or AI-assisted tools such as GitHub Copilot or Claude) to frame problems, sanity-check models, and move quickly. This role does not lead from a distance; familiarity with these tools is expected. 34; Accountable for delivery, not just direction: a track record of owning a product through to production and prioritizing under real constraints, balancing new work against operational stability, reliability, controls/regulatory needs, and technical debt. 34; Comfort with ambiguity and breadth: a track record of stepping into unfamiliar problem spaces and, in a space that is still forming, helping define it. 34; Proven ability to set product vision and drive execution across a highly matrixed organization; excellent executive stakeholder management and clear, structured written and verbal communication. Preferred qualifications, capabilities, and skills 34; Recognized thought leader within a related field 34; Experience leading a product team through a period of growth or ambiguity, standing up new areas, not just running established ones. 34; Hands-on experience with AI/ML and LLMs in production (agentic assistants, personalization, recommendations, or representation/embedding-based systems), including model evaluation and iteration. 34; Experience shipping data or platform products that support model developers or agentic assistants (context, signals, evaluation, or self-service tooling). 34; BS or MS in Engineering, Data Science, or a comparable field.