Senior Product Owner

SEI•Upper Providence Township, PA

3d•Hybrid

About The Position

The SEI Wealth Platform is seeking to hire a Senior Product Owner. As a Product Owner, you will primarily focus on executing on the product roadmap. As a Senior Product Owner for the Data & AI team, you will serve as the connective tissue between business strategy and delivery. You will be accountable for defining the business outcomes, priorities, and success measures for data and AI products, while partnering closely with engineering and analytics teams who execute the technical solutions. This role focuses on owning the “what” and “why”, ensuring that data pipelines, analytics platforms, and AI‑enabled capabilities deliver measurable value, are responsibly governed, and align with enterprise priorities. What you will do: Product Strategy & Roadmap -Define and communicate a clear product vision and roadmap for Data & AI initiatives, aligned to business goals and measurable outcomes. -Balance near‑term delivery with long-term platform and capability evolution. Backlog Ownership & Prioritization -Own and continuously refine the product backlog, translating business needs into epics, features, and user stories. -Make informed trade‑off decisions across scope, value, risk, and dependencies. Cross‑functional Collaboration -Act as the primary liaison between business stakeholders, data scientists, ML engineers, and data engineers. -Ensure shared understanding of goals, success criteria, and delivery expectations. Data & AI Lifecycle Oversight -Provide product ownership and oversight across the AI and analytics lifecycle—from data sourcing and preparation through model development, deployment, and ongoing improvement. -Ensure initiatives are aligned with business use cases, not just technical feasibility. Quality, Testing & User Feedback -Partner with delivery teams to ensure solutions meet business expectations and deliver usable, trusted insights. -Establish feedback loops with users and stakeholders to drive continuous improvement. Model Performance & Value Tracking -Own success metrics for AI‑enabled capabilities, including business impact, reliability, and usability. -Partner with technical teams to monitor outcomes such as accuracy, bias, and performance degradation over time. Data, AI Governance & Responsible Use -Work with risk, legal, and compliance teams to ensure AI solutions meet enterprise standards for privacy, security, and ethical use. -Ensure governance requirements are embedded into product design, not treated as afterthoughts. Technical Translation & Communication -Translate complex AI and data concepts (e.g., generative AI, LLM‑based solutions, AI agents) into clear business language, decisions, and priorities. -Enable non‑technical stakeholders to understand trade-offs, risks, and value without requiring technical depth.

Requirements

A minimum of 7 years of experience in Product Management or Product Ownership.
At least 2 years of experience owning products involving data, analytics, or AI‑enabled capabilities.
Demonstrated ability to articulate and deliver outcomes such as revenue growth, cost reduction, efficiency gains, or risk mitigation.
Comfortable engaging with senior stakeholders and tying delivery to business metrics.
Working knowledge of modern data and analytics ecosystems (e.g., data platforms, analytics pipelines, AI‑enabled products).
Conceptual understanding of AI/ML and generative AI sufficient to prioritize work, ask the right questions, and make informed decisions.
Ability to read and interpret metrics, dashboards, and basic data outputs; hands‑on coding is not required.
Ability to write requirements as prompts.
Ability to think like an AI engineer.
Strong command of Agile/Scrum practices, including backlog management, sprint planning, and stakeholder communication.
Experience working with tools such as Jira, Rally, or Azure DevOps.

Nice To Haves

Degree in a technical, quantitative, or business‑related field (Computer Science, Engineering, Data, Finance, or equivalent experience).
Experience in Financial Services, with exposure to wealth management, asset management, or alternatives.
Understand overall vision and how Product fits with that vision.
Familiarity with modern AI patterns (e.g., generative AI, AI‑assisted workflows, analytics platforms) from a product and governance perspective, not an implementation role.
Experience partnering with data governance, privacy, or risk teams; familiarity with enterprise governance concepts and tooling is a plus.
Experience interfacing with management.
Desire to work collaboratively with cross functional teams and other stakeholders.
Exceptional oral and written communication skills, including understanding of technical writing requirements
Someone who will embody our SEI Values of courage, integrity, collaboration, inclusion, connection and fun.

Responsibilities

Define and communicate a clear product vision and roadmap for Data & AI initiatives, aligned to business goals and measurable outcomes.
Balance near‑term delivery with long-term platform and capability evolution.
Own and continuously refine the product backlog, translating business needs into epics, features, and user stories.
Make informed trade‑off decisions across scope, value, risk, and dependencies.
Act as the primary liaison between business stakeholders, data scientists, ML engineers, and data engineers.
Ensure shared understanding of goals, success criteria, and delivery expectations.
Provide product ownership and oversight across the AI and analytics lifecycle—from data sourcing and preparation through model development, deployment, and ongoing improvement.
Ensure initiatives are aligned with business use cases, not just technical feasibility.
Partner with delivery teams to ensure solutions meet business expectations and deliver usable, trusted insights.
Establish feedback loops with users and stakeholders to drive continuous improvement.
Own success metrics for AI‑enabled capabilities, including business impact, reliability, and usability.
Partner with technical teams to monitor outcomes such as accuracy, bias, and performance degradation over time.
Work with risk, legal, and compliance teams to ensure AI solutions meet enterprise standards for privacy, security, and ethical use.
Ensure governance requirements are embedded into product design, not treated as afterthoughts.
Translate complex AI and data concepts (e.g., generative AI, LLM‑based solutions, AI agents) into clear business language, decisions, and priorities.
Enable non‑technical stakeholders to understand trade-offs, risks, and value without requiring technical depth.