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

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. 

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. 

Asset Management or financial services experience, and/or experience delivering solutions for a Client Service organization. 

Experience delivering AI/ML, generative AI, or conversational AI products—ideally in client servicing, contact center, or customer inquiry contexts. 

Familiarity with the modern AI stack—LLM platforms, vector databases, RAG pipelines, MLOps/LLMOps, and AI evaluation and observability tooling. 

Experience partnering with UX research and design to improve client journeys; familiarity with knowledge management or client lifecycle operations. 

AI-driven mindset and a passion for using AI copilots to raise product decisioning, documentation quality, and delivery velocity.