Lead Digital/Technical Product Manager - AI Product Solutions

USAA•Charlotte, NC

3d•Hybrid

About The Position

The Lead Digital/Technical Product Manager will join AI enablement teams in USAA’s Technology organization and lead a value chain of AI delivery from pilots through production. The role works with multiple lines of business, stakeholders, and delivery teams throughout the development lifecycle, serving several AI domains. It partners with business stakeholders, data scientists, and engineers to prioritize efforts, design and implement AI solutions, improve existing products, and manage dependencies and risks across the AI value chain. The position is accountable for understanding, prioritizing, and communicating business needs and technical solution designs to create AI enablement strategies, multi-year strategic roadmaps, multi-quarter tactical implementation plans, and feature-level backlogs for cross-functional teams of data scientists and AI engineers. It also guides the user experience and lifecycle of AI solutions, from adoption and onboarding through ongoing user engagement. This role requires strong critical thinking, problem-solving, technical knowledge, and the ability to work across delivery methodologies to plan and predictably deliver business value. The work environment is flexible but requires working in the office four days per week. The position may be based in San Antonio, Texas; Plano, Texas; Phoenix, Arizona; or Charlotte, North Carolina. Relocation assistance is not available. USAA does not provide visa sponsorship for this role.

Requirements

Bachelor’s degree, or four years of relevant education and/or experience.
Eight years of Digital/Technology Product Management experience, including Digital/Technology experience in Strategy/Planning, Innovation, User Experience, Process Engineering, Analytics, and/or Research/Testing; or an advanced degree and six years of such Product Management experience.
Comprehensive knowledge of the AI development lifecycle, including data science-run pilots, engineering productionalization and hardening, and emerging AI technologies.
Proven ability to develop, influence, present, and communicate highly complex business, digital, or technology concepts to lines of business, cross-functional teams, non-technical users, and senior leadership.

Nice To Haves

Eight or more years of experience delivering successful AI/Machine Learning products, or a combined eight or more years delivering successful products and consulting on AI technology and tooling.
Five or more years of experience in enterprise technology/engineering or Data Science at a major financial institution.
Five or more years of experience setting KPIs, objectives, and milestones and measuring progress in systems of record such as JIRA and Clarity.
Technical fluency in AI concepts and their business value, including experience across AI lifecycle phases, managing data requirements, discussing technical trade-offs with data scientists and AI engineers, developing use cases, and explaining how technology deliverables affect stakeholders’ business needs.
Proven success designing AI solutions and planning delivery, including prioritizing business needs, drafting solution designs, collaborating with architects, data scientists, and engineers, setting milestones, and drafting features for milestone delivery.
Experience engaging and influencing cross-functional teams and leaders, including data scientists and engineers, across geographically distributed work environments to deliver working technology.
A demonstrated bias toward informed action, including taking initiative to understand ambiguous business challenges or technical needs, planning a path forward, and generating buy-in for impactful action.
US military experience through military service or as a military spouse/domestic partner.

Responsibilities

Use Digital or Technology research, business and market intelligence, and data-driven insights to identify opportunities, propose solutions, create and maintain roadmaps, and improve performance.
Lead teams and stakeholders in envisioning, defining, and translating product and experience opportunities into initiatives such as requirements, epics, and features, and drive execution strategy.
Guide complex, integrated, and strategic product and experience opportunities from idea through market validation in collaboration with relevant stakeholders and subject matter experts.
Drive, quantify, and defend product and experience investments through business cases, hypotheses, and communication of value propositions.
Use data-driven problem-solving and analytical techniques to assess and report product and experience performance against KPIs and KRIs, and share best practices.
Guide teams in generating insights to identify improvement opportunities and follow through to ensure applicable improvements are implemented.
Prepare compelling presentations and communications for diverse audiences, including senior leadership, and facilitate product definition, concept, and collaboration sessions.
Coordinate discovery, development, delivery, and validation activities and mitigate risks and dependencies.
Conduct internal and external research and situational analysis to identify and apply industry best practices and trends.
Manage ambiguity, influence stakeholders to consider the broader picture, and drive clarity, solutions, and execution plans.
Maintain and apply expert knowledge of the business, technology, user experience, relevant processes, and product management.
Guide and mentor team members, act as an escalation point, and ensure issues are resolved.
Ensure risks associated with business activities are identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.
Partner with lines of business and stakeholders to prioritize project- and feature-level work across AI domains.
Partner with stakeholders, data scientists, and engineers to design and drive implementation of AI solutions.
Create AI enablement strategies, multi-year strategic roadmaps, multi-quarter tactical plans, and feature-level backlogs spanning multiple quarters.
Guide user experience and lifecycle for assigned AI solutions, including adoption, onboarding, and ongoing engagement.