Corporate Vice President - Private Markets Technology

New York Life•New York, NY

1d•$185,000 - $264,500•Hybrid

About The Position

Within the Tech, Data, AI, Ventures (TDAV) organization, this role 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. TDAV builds solutions that power how New York Life serves 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. New York Life is advancing an AI-led transformation of Portfolio Management across OCIO and NYLIM, connecting proprietary investment expertise, portfolio information, data, analytics, and AI to improve investment decisions and scale differentiated institutional knowledge. The Private Markets domain focuses on the proprietary information, underwriting and credit workflows, deal and deployment processes, portfolio monitoring, and investment knowledge associated with private-market investing. A central opportunity is to make insights that often reside within individual teams, documents, and workflows more discoverable, reusable, and actionable while preserving appropriate confidentiality, controls, and investment judgment. This Corporate Vice President, Private Markets Technology provides senior end-to-end technology leadership for the Private Markets domain within New York Life's AI-led Portfolio Management transformation. The leader owns the technology strategy and roadmap spanning private-market workflows, proprietary data and documents, analytics, AI, agentic capabilities, integration, engineering, controls, and production readiness. This role is expected to reimagine how technology supports sourcing, underwriting, credit analysis, investment committee preparation, deal execution, portfolio monitoring, and reuse of proprietary investment intelligence. The leader will work across investment teams, Technology, Data & Analytics, Architecture, Risk, Compliance, Legal, and strategic partners to convert differentiated private-market knowledge into scalable capabilities. The role requires strong judgment around sensitive and unstructured information, provenance, entitlements, confidentiality, explainability, and where AI can accelerate research and analysis without weakening investment accountability or controls.

Requirements

15+ years of progressively responsible experience in technology, software engineering, solution delivery, digital transformation, investment technology, data/analytics, or related disciplines, including significant leadership responsibility in complex enterprise environments.
Proven experience owning end-to-end technology capabilities or major transformation domains from strategy through architecture, engineering, implementation, production, adoption, and measurable outcomes.
Demonstrated success operating in federated or matrixed enterprises and influencing senior business and technology stakeholders across organizational boundaries.
Broad technology and engineering background spanning modern application development, APIs and integration, data and analytics, cloud platforms, enterprise architecture, automation, and emerging technologies.
Strong understanding of generative AI, agentic technologies, and AI-assisted engineering and their practical application to enterprise workflows and software delivery.
Experience leading multidisciplinary technology teams and managing strategic partners, contractors, and federated resources.
Experience making consequential technology, architecture, investment, sequencing, and resource tradeoffs in complex transformation environments.
Experience operating within enterprise Risk, Security, Compliance, Audit, and control environments.
Strong analytical, communication, prioritization, systems-thinking, and executive-influence capabilities.
Domain Technology Leadership - Owns the complete technology capability for a major business domain, not simply an application portfolio.
Technology & Engineering Judgment - Evaluates solution approaches, architecture and data implications, engineering tradeoffs, and production risks.
Systems Thinking - Understands interactions across business workflows, applications, data, analytics, architecture, controls, and operating models.
AI-Enabled Transformation - Identifies where AI and agentic capabilities can materially change workflows, decisions, and delivery economics.
Executive Communication & Influence - Creates clarity, drives decisions, and influences senior stakeholders across organizational boundaries.
Strategic Planning & Execution - Converts future-state ambition into prioritized roadmaps, investments, sequencing, and executable outcomes.
Enterprise Delivery Discipline - Applies SDLC, Agile and Lean principles pragmatically with strong dependency, release, and production-readiness management.
Talent & Partner Leadership - Builds strong teams and integrates internal, federated, and external engineering capacity around common outcomes.
Risk & Control Judgment - Integrates security, data governance, compliance, auditability, and operational resilience into technology decisions from inception.
People Management – Develops and empowers talent through clear expectations, actionable feedback, effective coaching, and accountability, while fostering an inclusive, high-performing team environment.

Nice To Haves

Experience with private markets, private credit, real estate, alternatives, underwriting, or related investment technology.
Experience with document intelligence, knowledge management, unstructured data, or AI-enabled research workflows.
Familiarity with investment underwriting, credit analysis, diligence, portfolio monitoring, or investment committee processes.
Experience operating in environments with sensitive investment information, complex entitlements, and regulatory controls.

Responsibilities

Own the end-to-end technology strategy and roadmap for Private Markets across sourcing, underwriting, credit analysis, investment committee workflows, deal execution, portfolio monitoring, and related investment processes.
Create scalable capabilities that make proprietary private-market information and insights more discoverable, reusable, and actionable across authorized investment teams.
Reimagine document-heavy and expert-driven workflows using data, analytics, automation, and AI while preserving investment judgment and confidentiality.
Enable better visibility into portfolio exposures, investment performance, risks, covenants, market developments, and emerging opportunities.
Establish measurable outcomes tied to investment productivity, speed to insight, reuse of proprietary intelligence, workflow efficiency, and decision effectiveness.
Define agentic and AI-enabled patterns for researching, synthesizing, comparing, and monitoring private-market information across structured and unstructured sources.
Enable agents and analytical tools to extract and connect relevant information from investment memos, financials, diligence materials, legal documents, research, and portfolio data with appropriate provenance.
Determine where deterministic calculations and controlled models are required versus where generative AI and agentic reasoning add value.
Ensure outputs are traceable to authoritative sources and appropriately controlled for confidential, material non-public, or otherwise restricted information.
Partner with enterprise AI governance, Risk, Compliance, Legal, and Security on responsible deployment.
Partner with Solution Engineering & Architecture to define scalable solution patterns for private-market applications, workflows, document intelligence, agents, APIs, and integrations.
Partner with the cross-bet Data Architect on proprietary data products, metadata, semantics, lineage, document architectures, access patterns, and entitlements.
Integrate private-market data with portfolio, market, risk, finance, and other enterprise information where appropriate.
Make build, buy, reuse, platform, integration, and AI technology decisions with consideration for security, portability, scalability, and long-term sustainability.
Ensure solutions are observable, resilient, auditable, secure, and production ready.
Partner with the Business Discovery & Solution Strategy Lead for Private Markets, Reporting & Distribution to translate future-state investment workflows into executable technology roadmaps.
Use prototypes and working solutions to validate user needs, AI behavior, data access, and architecture assumptions before scaling.
Lead multidisciplinary outcome teams involving Product Owners, Forward Deployed Analysts, Forward Deployed Engineers, Obin AI engineers, investment SMEs, Data SMEs, and Risk/Control partners.
Drive workforce and capacity planning and manage strategic technology partners with clear accountability for outcomes, quality, and knowledge transfer.
Maintain continuity from discovery through production and adoption.
Build trusted relationships with senior private-market investment leaders and translate strategic priorities into technology investments and delivery commitments.
Balance enterprise reuse with the specialized workflows and confidentiality requirements of individual investment teams.
Embed security, entitlements, information barriers, Compliance, Legal, Risk, model governance, and auditability into the solution lifecycle.
Establish transparent measures of adoption, workflow efficiency, quality, investment-team productivity, and business value.
Build, lead and develop high performing teams with strong domain knowledge and modern technology and engineering capabilities.
Attract, develop and retain forward deployed and other high caliber technology talent, while building technology leadership and domain expertise across the organization.
Establish clear accountability and a culture of collaboration, innovation, engineering discipline and continuous improvement.
Lead effectively through both direct authority and matrixed relationships across Business, Product, Technology and partner teams.