Product Manager, AI-Driven ML Infrastructure

Capital One; Richmond (VA); On-site; USD 150,000 - 171,000; Full time

Job summary

Capital One is seeking a Manager of Product Management in Richmond, VA to lead ML-powered product initiatives within the Card Intelligence team. You’ll collaborate with data scientists and engineers to shape scalable AI-driven customer experiences and drive business outcomes.

Responsibilities include defining problem statements, aligning stakeholders, roadmapping ML infrastructure, and delivering reusable, resilient capabilities while managing risk and leveraging data to measure success.

Qualifications

• Degree or equivalent in a quantitative field or business/marketing.
• OR Bachelor's with 3+ years PM experience.

Responsibilities

• Define clear problem statements and drive iterative software delivery.
• Use customer insights to shape priorities and align stakeholders.
• Own and prioritize three-year ML infrastructure and model delivery roadmaps tied to business outcomes.
• Identify leverage points in complex problems and define success metrics with data.
• Balance risks while innovating in a well-managed way.
• Partner with technology teams on design decisions for white space technical problems.
• Share business strategy and roadmap with Tech partners and lead agile ceremonies with the Tech Lead.
• Deliver value with reusable, extensible capabilities and identify opportunities when metrics underperform.

Skills

Product Management

Stakeholder alignment

Roadmap planning

Education

Bachelor's or Master’s Degree in quantitative field or Business/Marketing

3+ years Product Management experience (if no quant degree)

Tools

ML infrastructure

MLOps

Data engineering platforms

Job description

Capital One is seeking a Manager of Product Management in Richmond, VA to lead ML-powered product initiatives within the Card Intelligence team. You’ll collaborate with data scientists and engineers to shape scalable AI-driven customer experiences and drive business outcomes.

Responsibilities include defining problem statements, aligning stakeholders, roadmapping ML infrastructure, and delivering reusable, resilient capabilities while managing risk and leveraging data to measure success.