You enjoy shaping the future of product innovation as a core leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our dynamic team and make a meaningful impact by delivering high-quality products that resonate with clients.
As a Product Manager within the Personalization & Customer Insights Team, you are an integral part of the team that innovates new product offerings and leads the end-to-end product life cycle. As a core leader, you are responsible for acting as the voice of the customer and developing profitable products that provide customer value. Utilizing your deep understanding of how to get a product off the ground, you guide the successful launch of products, gather crucial feedback, and ensure top-tier client experiences. With a strong commitment to scalability, resiliency, and stability, you collaborate closely with cross-functional teams to deliver high-quality products that exceed customer expectations.
You will drive the strategy and execution of ML‑powered personalization and next‑best‑action capabilities for marketing and servicing across mobile, web, contact center, branch, and marketing channels. You will translate research and analytics into clear roadmaps, partner with Data Scientists and ML Engineers to design and iterate LLM/ML models, run experiments to validate impact, and deliver measurable outcomes while upholding privacy, consent, and fairness standards. You will own and drive key ML personalization initiatives—such as recommendation and next-best-action models, agentic capabilities/solutions, and supporting model development lifecycle (MDLC) infrastructure. Partner with Data Science, ML Engineering, Channel teams and Business Stakeholders to shape roadmaps, prioritize opportunities, iterate on models, run experiments to validate impact, and operationalize scalable, reliable solutions across channels while upholding privacy, consent, and fairness standards.
Job responsibilities
- Develops a product strategy and product vision that delivers value to customers
- Manages discovery efforts and market research to uncover customer solutions and integrate them into the product roadmap
- Owns, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition
- Builds the framework and tracks the product's key success metrics such as cost, feature and functionality, risk posture, and reliability
- Define and own the strategy and roadmap for operating‑memory data products: what customer signals are needed, how they are represented, governed, and retrieved safely, and how they are delivered to the agent and domain agents at the right time
- Partner with the Channel/Interface team on how memory and context are used in the assistant experience; align on contracts, SLAs, and guardrails to ensure reliability and safe use
- Lead the development of ML‑powered personalization and next‑best‑action capabilities for marketing and servicing; frame hypotheses, prioritize use cases, and measure impact through experimentation
- Translate user research and analytics into epics, user stories, and acceptance criteria; manage the product backlog and delivery across discovery, launch, and continuous improvement
- Collaborate closely with Data Scientists and ML Engineers across the model lifecycle (design, training, evaluation, deployment, monitoring) for LLM/ML‑based solutions
- Champion an API‑ and event‑driven architecture on cloud infrastructure to ensure scalable, reliable delivery of context and signals across channels. Establish and track product KPIs for engagement, quality, and business outcomes; ensure delivery against time, cost, and quality targets
- Uphold responsible AI and data practices in partnership with risk, privacy, and compliance teams, including consent management, and fairness to provide people leadership, mentor product managers, foster a culture of experimentation and measurable outcomes, and influence cross‑functional partners
Required qualifications, capabilities, and skills
- 5+ years of experience or equivalent expertise in product management or a relevant domain area
- Advanced knowledge of the product development life cycle, design, and data analytics
- Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
- Proven product management leadership delivering AI‑powered products to production in customer‑facing environments, in close partnership with Data Scientists and ML Engineers across the model lifecycle
- Experience defining and shipping data products that support agentic assistants (e.g., operating memory, context, or signals) and integrating them with partner teams that own the channel experience
- Demonstrated success with ML‑driven personalization and next‑best‑action for marketing and/or servicing, including experimentation (e.g., A/B testing) and outcome measurement
- Proficient knowledge of the product development life cycle, including discovery, requirements definition, and backlog management (epics, user stories, refinement, PBR, JIRA)
- Strong data literacy and the ability to turn user research, journey insights, and product metrics into decisions and roadmaps that deliver on time, cost, and quality
- Excellent people leadership and stakeholder management: mentoring product managers and influencing partners across Product, Design, Engineering, Marketing/Servicing, and business teams
- Experience with API‑first delivery on cloud (e.g., AWS) and coordination across multi‑channel experiences (mobile, web, contact center, branch, marketing). Clear, structured communicator with strong written and presentation skills
Preferred qualifications, capabilities, and skills
- Demonstrated prior experience working in a highly matrixed, complex organization
- Hands‑on experience with AI/ML and LLMs in production (e.g., agentic assistants, personalization, recommendations), including model evaluation and iteration with Data Science partners
- Experience with conversational/agentic systems, operating‑memory or context architectures, and recommendation systems for marketing/servicing
- BS or MS in Engineering, Data Science, or a comparable field of study
- Knowledge of current digital banking trends and customer experience patterns, and familiarity with privacy, consent, and fairness considerations in AI