Strategic AI Product Leader, Enterprise Platforms

Edward Jones; Washington (District of Columbia); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Bonus potential

Employee Assistance Program

Job summary

Edward Jones seeks a Lead AI Product Manager to shape and lead the firm's most complex enterprise AI products and reusable capabilities spanning multiple domains, experiences, and workflows.

You will own AI product strategy, discovery, prioritization, adoption, lifecycle management, and measurable value, collaborating with engineering, data, risk, and business teams to deliver scalable, explainable AI.

Qualifications

• Bachelor's degree in a relevant technical, product, analytics, or business field or equivalent experience.
• 7+ years of Product Management experience in complex technology, platform, data, digital, or AI products.
• 3+ years of experience with AI, ML, generative AI, agentic systems, intelligent automation, AI platforms, or advanced analytics.

Responsibilities

• Define vision, value proposition, outcomes, priorities, roadmap for strategic AI products and domains.
• Translate priorities into scalable product strategies, requirements, and prioritized work.
• Lead continuous discovery, prototypes, experiments, and pilots to validate ideas.
• Own full product lifecycle from discovery to adoption, ensuring customer value and measurable outcomes.
• Define end-to-end product experience including interfaces, data needs, and governance.

Skills

AI product management

Cross-functional leadership

Strategy & roadmaps

Discovery & experimentation

Data-driven decision making

Stakeholder management

Education

Bachelor's degree in technical, product, analytics, or business

Tools

Microsoft Copilot Studio

Power Platform

Azure AI

Databricks

Job description

Edward Jones seeks a Lead AI Product Manager to shape and lead the firm's most complex enterprise AI products and reusable capabilities spanning multiple domains, experiences, and workflows.

You will own AI product strategy, discovery, prioritization, adoption, lifecycle management, and measurable value, collaborating with engineering, data, risk, and business teams to deliver scalable, explainable AI.