Vice President, Product Manager - Asset Management Client Service Experience - Service Assist

Job Responsibilities

• Define and maintain the Service Assist product vision, strategy, and roadmap, aligned to business priorities, client experience goals, and the firm's AI ambitions.
• Champion an AI-first approach, identifying and prioritizing high-value use cases for generative AI, machine learning, and intelligent automation—such as intelligent intake and routing, conversational AI and virtual assistants, response generation, summarization, and retrieval-augmented generation (RAG).
• Lead transformation and change management, driving user adoption and operational readiness across servicing teams.
• Partner with cross-functional triad teams—Engineering, Design, and Data Science, alongside Service operations and Risk & Controls—to ensure alignment, clear ownership, and successful delivery.
• Run an agile governance and delivery cadence, and own and prioritize the product backlog, balancing scope, timelines, dependencies, and risk.
• Define OKRs and success metrics—response time, accuracy and quality, automation and deflection rates, risk reduction, and client satisfaction (CSAT)—and use experimentation and A/B testing to drive continuous improvement.
• Establish AI evaluation frameworks, guardrails, and monitoring—covering model accuracy, hallucination and bias mitigation, explainability, and responsible-AI compliance.
• Translate complex AI, data, and technology concepts into clear narratives for senior and non-technical audiences, proactively managing stakeholders and driving timely decisions and escalations.

Required Qualifications, Capabilities, And Skills

• Proven, hands-on product management experience with agile practices—roadmaps, backlogs, prioritization, and iterative delivery.
• Strong conceptual command of AI/ML, generative AI, and large language models (LLMs)—including prompt engineering, retrieval-augmented generation (RAG), and agentic AI—and the ability to translate them into product features such as automation, summarization, intent classification, and intelligent routing.
• High data literacy, with the ability to interpret data, define metrics, and draw sound conclusions across quality, cycle-time, and adoption measures.
• Ability to use AI-assisted and no-code/low-code tooling to independently produce data-science-style outputs—queries, analyses, experiments, and prototypes—without deep hands-on coding.
• Working knowledge of responsible AI, model evaluation, and AI governance, partnering with Data Science and Engineering on model performance and safe deployment.
• Excellent written and verbal communication, documentation, analytical thinking, and sound judgment.
• Proven ability to influence and lead through others, manage cross-functional teams, and navigate conflict.