Data Product Manager

Capital Group•Los Angeles, CA

3d•Hybrid

About The Position

Within the broader Insights and Analytics team, the Data Enablement team functions as part of the Insights and Analytics organization and plays a critical role in advancing our data and AI capabilities to support Global Client Groups. We focus on making trusted, relevant, and well-governed data easily accessible to stakeholders across Sales, Marketing, Digital Experience, and Servicing. Through thoughtful design and delivery of standard, reusable data and AI-powered products, we empower downstream teams with robust self-service capabilities, enable advanced analytics and automation, and unlock new business use-cases. As a Data Product Manager, AI & Data Products, you will shape and evolve our data and AI product ecosystem, ensuring the availability of high-quality data and scalable AI/ML-ready foundations. This role is pivotal in driving the modernization of our data platform and enabling intelligent, data-driven experiences across the distribution lifecycle. You will partner closely with Product, Engineering, Data Science, and Architecture to gain a deep understanding of our technology ecosystem and identify high-impact opportunities where data, analytics, and AI can improve decision-making and user outcomes. A key focus in the near term will be supporting strategic re-platforming and modernization initiatives that enable a single source of truth for client and business data, while laying the groundwork for AI-driven insights, predictive capabilities, and automation. You will challenge the organization to think boldly about how data and AI can create differentiated value, while also delivering reliable, well-governed data products that meet today’s business needs.

Requirements

5+ years of Product Management experience, with a proven track record of launching and scaling data platforms and/or AI-enabled products.
Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related field; Master’s degree preferred.
Experience managing B2B or B2B2C data and AI products throughout the full product lifecycle, ideally within Financial Services, Asset Management, or another highly regulated industry.
Hands-on experience working with data platforms, analytics tools, and AI/ML ecosystems (e.g., data warehouses/lakes, feature stores, model pipelines, MLOps concepts).
Ability to perform and interpret data analysis and profiling using SQL, Python, and data visualization tools to inform product decisions.
Strong ability to prioritize and communicate product roadmaps across diverse business functions and geographies.
Comfort operating in ambiguity, with the ability to adapt product strategy as AI capabilities, business needs, and partner priorities evolve.
Exceptional written and verbal communication skills, with the ability to influence audiences ranging from senior leaders to engineers and data scientists.
Ability to inspire and lead cross-functional teams, including product managers, architects, engineers, data scientists, and business partners.
High curiosity and a desire to deeply understand and simplify complex data and AI domains.
Proficiency in applying both qualitative and quantitative methods to define, measure, and continuously improve product success.
Entrepreneurial mindset with a bias for action—comfortable rolling up your sleeves and turning incomplete or conflicting inputs into clear execution plans.
A high bar for product excellence, with strong attention to detail and a focus on delivering reliable, scalable, and trustworthy data and AI products.

Nice To Haves

Master’s degree preferred.

Responsibilities

Define and execute a comprehensive data and AI product strategy, synthesizing market trends, user needs, and stakeholder input into a cohesive vision and execution plan.
Translate strategy into a clear product roadmap, prioritizing data and AI capabilities that deliver measurable impact for users and the business.
Manage the end-to-end product development lifecycle, delivering data and AI-enabled features iteratively to solve real business problems.
Partner closely with Data Science and Engineering teams to enable AI/ML use-cases such as advanced analytics, personalization, predictive modeling, and intelligent automation.
Establish strong partnerships with business and technology stakeholders, creating continuous feedback loops to refine product value and adoption.
Develop a deep understanding of upstream and downstream systems to shape data models, APIs, and AI consumption patterns.
Ensure thoughtful product evolution, addressing product lifecycle management, adoption and migration experiences, and clear enablement for users.
Drive product discovery, requirements definition, and feature prioritization using modern product and agile best practices, informed by data and user insights.