Corporate Vice President - Product Analytics Lead

Location Designation: Hybrid - 3 days per week Technology, Data, AI and Ventures: 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. Role Overview The Product Analytics Lead is a high-impact role responsible for shaping product strategy and improving customer and business outcomes through data. This individual will serve as a dedicated analytics partner to product and executive leadership, bringing structure, rigor, and clarity to complex product decisions. The role translates product vision and customer needs into measurable goals, analytical roadmaps, experiments, and actionable recommendations. It connects insights across the product lifecycle, from opportunity discovery and prioritization through launch, adoption, engagement, and ongoing optimization. What You'll Do: Product Strategy and Opportunity Framing • Translate product vision, customer needs, and ambiguous business questions into clear problem statements, hypotheses, and measurable outcomes. • Partner with product leaders and cross-functional stakeholders to identify customer pain points, size opportunities, refine scope, and prioritize the product roadmap. • Define product success measures, including north-star metrics, input metrics, guardrails, and decision criteria. Product Analytics and Insight Generation • Lead end-to-end product analysis across discovery, development, launch, adoption, engagement, retention, and value realization. • Integrate product behavioral data with customer, operational, financial, and market data to create a holistic view of product performance. • Build segmentation, funnel, cohort, journey, and driver analyses that reveal friction points and opportunities for improvement. • Apply iterative analytical approaches to test hypotheses, deepen customer understanding, and refine product recommendations. Experimentation and Product Optimization • Partner with product, design, and engineering teams to design experiments and establish sound measurement plans before launch. • Evaluate feature and experience changes using A/B testing, quasi-experimental methods, and other appropriate techniques. • Turn experiment results and post-launch performance into clear recommendations for scaling, iterating, or retiring product capabilities. • Create rapid learning loops that help teams make evidence-based trade-offs and continuously improve the customer experience. Product Measurement and Data Foundations • Own the product measurement framework and ensure metrics are clearly defined, consistently interpreted, and tied to customer and business value. • Partner with engineering and data teams to improve event instrumentation, data quality, semantic consistency, and analytical usability. • Develop scalable dashboards and self-service decision tools while reserving deep-dive analysis for the highest-value product questions. • Promote responsible use of data, including appropriate governance, privacy, and controls throughout the product lifecycle. Executive and Product Communication • Synthesize findings into concise, decision-oriented narratives for product and senior leadership. • Translate complex analysis into product implications, customer impact, risks, trade-offs, and recommended actions. • Facilitate product reviews, roadmap discussions, and working sessions with clear evidence and a strong point of view. Ways of Working • Customer-centered: starts with customer needs, behaviors, and outcomes. • Product-minded: balances desirability, viability, feasibility, and risk when framing recommendations. • Highly iterative: develops insights through cycles of discovery, experimentation, measurement, and refinement. • Cross-functional: collaborates closely with product management, design, engineering, data, analytics, operations, and business stakeholders. • Outcome-oriented: focuses on measurable product and customer impact rather than analysis or feature delivery alone. • Agile and responsive: supports planned roadmap priorities and emerging product decisions. What You'll Bring: • 6-10+ years in product analytics, product management, data science, strategy, consulting, or a related field. • Experience influencing product strategy and roadmap decisions through customer insight, analytics, and experimentation. • Experience working on complex, ambiguous product problems with senior stakeholders and cross functional teams. • Background in financial services, insurance, or another regulated industry is preferred. • Strong understanding of product discovery, product development, lifecycle measurement, and outcome based prioritization. • Advanced analytical toolkit, including SQL, Python or R, experimentation, statistics, and data visualization. • Experience with product analytics methods such as funnels, cohorts, segmentation, retention, journey analysis, and causal inference. • Experience working with large, complex, and fragmented datasets, including product instrumentation and event-level data. • Ability to structure and execute end-to-end analyses independently, from question framing through recommendation and impact measurement. • Exceptional executive communication, product storytelling, and decision-framing skills. • Strong stakeholder management and the ability to influence product and senior leaders without direct authority. • Ab