Technical Product Manager - AI Agents in ASKB

Bloomberg•New York, NY

14h•$140,000 - $295,000•Onsite

About The Position

ASKB is a conversational AI workspace inside the Bloomberg Terminal that allows users to search, analyze, and automate financial research using natural language. We are looking for a creative, strategic, and hands-on Product Manager to lead the client experience for Agents in ASKB. The goal is to help users, teams, and firms move beyond asking ASKB questions and delegate meaningful work to it. When they find a valuable way of working, ASKB should make it easy to reuse and scale across their organization. You will own how clients experience these capabilities in ASKB. Customers should not need to understand the underlying AI or spend time configuring it like software. ASKB should understand what they are trying to accomplish and do as much of the work as possible to get them to a useful result. You will shape how firms share, scale, and manage these capabilities across their organization. You will work directly with clients and partner closely with the teams building the underlying models and agent platform, as well as UX, Product, Sales, Market Specialists, and Support. The right candidate combines strong product judgment and client empathy with enough technical depth to understand how new AI capabilities can create meaningful value for Bloomberg customers.

Requirements

At least 8 years of product management or equivalent product leadership experience, with a track record of building complex client-facing products.
Experience in enterprise software, workflow products, productivity tools, or platform products is particularly relevant.
Strong product judgment and experience setting strategy, building roadmaps, and taking products from early customer discovery through launch, adoption, and ongoing improvement.
Experience working directly with clients to understand their workflows and turn ambiguous needs into clear product decisions.
Experience making sophisticated capabilities feel intuitive without taking away the flexibility advanced users need.
Experience leading highly cross-functional work across Engineering, UX, AI, go-to-market teams, and senior stakeholders.
Comfortable driving decisions and influencing direction without direct authority.
Practical fluency in generative and agentic AI.
Understanding of models, context, tools, permissions, and evaluation to make good product decisions and actively experiment with new AI products as the technology evolves.

Nice To Haves

A degree in Computer Science, Computer Engineering, a related technical field of study, or equivalent work experience.
Experience building AI-native or agentic products, particularly products that help non-technical users delegate work and get useful results with limited supervision.
Experience with authoring tools, low-code products, creator platforms, or other products where users can create, share, and reuse capabilities.
Experience with enterprise collaboration, administration, permissions, governance, or other products that need to work across both individual users and large organizations.
Knowledge of financial markets and Bloomberg client workflows, or familiarity with the Bloomberg Terminal and Bloomberg’s broader product ecosystem.

Responsibilities

Own the vision, strategy, roadmap, and success measures for customer experience of Agents in ASKB.
Keep the product focused on simplifying the experience and growing adoption of the feature.
Stay close to clients and understand the work they want to delegate to ASKB.
Turn client needs into clear product decisions and experiences that feel simple even when the underlying technology is complex.
Shape how clients create, use, share, and get value from agents.
Build experiences that work for someone using an agent for the first time while still giving advanced users the control they need.
Define how firms organize and manage agents across their organization.
Make sure the product supports the sharing, permissions, administration, and controls required by enterprise customers.
Use product data, client research, evaluations, and hands-on experimentation to understand what is working and what is not.
Lead pilots and launches, adjust the roadmap as we learn, and communicate priorities and trade-offs clearly across teams.