Corporate Vice President - Technical Product Manager

New York Life•New York, NY

1d•$119,000 - $170,000•Hybrid

About The Position

Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes. Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last. We build intelligence products that help field professionals make better decisions — not static reports or generic dashboards, but purpose-built surfaces that deliver the right context at the point of action. The quality of those surfaces determines whether our work reaches users at all. This role exists because that bar matters, and because getting it right at scale requires someone who can hold the thread from boardroom priorities down to data pipeline behavior. The Senior Technical Product Manager on this team owns the product agenda for Decision Intelligence end to end: the strategic narrative that earns executive sponsorship, the roadmap that a squad of engineers, data scientists, and designers can actually execute against, and the judgment to know when a technical shortcut is a smart trade-off versus a debt that will sink the product later. You are not translating between business and engineering — you are fluent in both, and you are the person each side trusts to make the call. This is a leadership role inside a small, high-ownership team embedded in a large enterprise. You will set direction for other product professionals, represent the portfolio to senior leadership, and remain close enough to the technical fabric — data models, APIs, ML outputs, system architecture — to stress-test what your team proposes before it reaches a steering committee. If you want a role that moves fluidly between a whiteboard with engineers and a slide deck for the C-suite, this is the right environment.

Requirements

12-15 years of experience across product management, technical program leadership, or engineering roles, including significant time operating as a technical product leader for data- or AI/ML-driven products
Demonstrated success setting product strategy and roadmap at a portfolio or platform level, with direct accountability to senior leadership or executive stakeholders
Fluency across the full stack of a modern product organization: front-end and back-end architecture, APIs and data contracts, and the fundamentals of how ML/AI models are built, evaluated, and deployed
Track record of leading and developing cross-functional teams — product managers, designers, engineers, and data scientists — including people management or strong technical-lead experience
Exceptional ability to move between altitudes in the same day: a technical architecture review in the morning, an executive steering committee in the afternoon
Experience partnering with design on UX strategy and holding a quality bar across a portfolio of products, not a single surface
Comfort with ambiguity and the judgment to prioritize ruthlessly in a resource-constrained, high-ownership team
Prior experience in financial services, insurance, or another highly regulated enterprise environment
Hands-on familiarity with React/TypeScript front-ends and Python-based data or ML pipelines, sufficient to review technical designs credibly
A history of building the product function itself — hiring, mentoring, and setting process for a growing team
Executive presence developed through direct board-, C-suite-, or investor-facing responsibility
Portfolio-level roadmapping, prioritization frameworks, and business-case development
Executive communication: narrative building, steering-committee facilitation, board-ready materials
Team leadership: hiring, coaching, performance management, and cross-functional facilitation
Front-end architecture (React/TypeScript ecosystems): component design, performance, accessibility at a review level
Back-end and data architecture: REST/GraphQL API design, data contracts, service boundaries, system scalability
AI/ML product fluency: model lifecycle, evaluation metrics, explainability, and the operational realities of shipping model-driven features
Enterprise engineering practice: CI/CD, cloud infrastructure basics, and how a large regulated enterprise ships software safely
UX strategy and design-system thinking (Figma-level fluency preferred, not required)
Data governance, model risk, and compliance considerations specific to financial services and insurance

Nice To Haves

Figma-level fluency preferred, not required

Responsibilities

Set the multi-year product vision and strategy for the Decision Intelligence portfolio, translating enterprise priorities and field-professional needs into a roadmap that sequences investment across multiple product lines.
Serve as a primary product voice in front of senior leadership and business stakeholders — framing trade-offs, defending prioritization decisions, and translating technical constraints into business terms that non-technical executives can act on.
Lead and grow a cross-functional pod of product managers, designers, engineers, and data scientists — setting working norms, unblocking execution, and coaching more junior product talent.
Partner directly with engineering leads and data scientists on architecture decisions, model design, and data contracts — not as an approver at the end of the process, but as a participant early enough to shape it.
Work closely with design leadership to set UX standards across the portfolio, review and challenge wireframes and prototypes, and ensure user validation happens before teams build at full fidelity.
Set the strategy for shared components, data services, and design patterns that are reused across the full canon of intelligence products, and hold other teams accountable to building for reuse rather than rebuilding in isolation.
Partner with data science and risk/compliance stakeholders to define what "production-ready" means for a model-driven surface — accuracy thresholds, explainability requirements, monitoring, and the honest communication of uncertainty to end users, particularly in a regulated financial services context.