Head of Personalization Intelligence

• Define and lead the strategy for the data and intelligence capabilities that enable personalization.

• Define and lead the strategy for the data and intelligence capabilities that enable personalization.
• Create a connected ecosystem across signals, client understanding, value, analytics, and measurement rather than a collection of individual models or use cases.
• Establish common data, analytical, and measurement frameworks that can be reused across products and channels.
• Advance the organization toward increasingly dynamic, individual-level and real-time client intelligence.

• Lead the development of personas, segments, client insights, and other analytical frameworks that deepen understanding of client needs, behaviors, preferences, and opportunities.

• Lead the development of personas, segments, client insights, and other analytical frameworks that deepen understanding of client needs, behaviors, preferences, and opportunities.
• Integrate behavioral, transactional, engagement, and other available data to create a more holistic view of the client.
• Translate complex data into actionable insights that Product, Marketing, and other partners can use to inform their strategies.

• Lead the development of signals that identify changes in client behavior, needs, intent, engagement, and opportunity.

• Lead the development of signals that identify changes in client behavior, needs, intent, engagement, and opportunity.
• Establish scalable, reusable signal frameworks that can support multiple products, experiences, and channels.
• Determine which signals should evolve toward real-time availability based on their potential client and business value.
• Establish appropriate standards and governance for personalization data and signals.

• Lead the development and evolution of lifetime-value methodologies.

• Lead the development and evolution of lifetime-value methodologies.
• Bring together client value, needs, behaviors, and signals to provide a richer understanding of both current and potential client relationships.
• Develop analytical approaches that balance near-term opportunities with longer-term client value.

• Partner with Technology and Enablement to translate personalization intelligence into scalable data and analytical capabilities that can be consumed across channels.

• Partner with Technology and Enablement to translate personalization intelligence into scalable data and analytical capabilities that can be consumed across channels.
• Define the data, signal, model, and analytical requirements necessary to move from batch-oriented insights toward more dynamic and real-time intelligence.
• Partner with Technology to shape the data and analytical architecture required to make insights available at the point of interaction.
• Ensure personalization intelligence is structured in ways that can be consistently understood and consumed by downstream platforms and channels.

• Establish a consistent measurement framework for personalization across the organization.
• Develop methodologies for measuring incrementality, client outcomes, business outcomes, and longer-term value.
• Advance experimentation and test-and-learn approaches that distinguish correlation from incremental impact.
• Create feedback loops that incorporate channel interactions and outcomes back into signals, personas, models, lifetime value, and future insights.
• Use measurement not simply to report performance, but to continuously improve the underlying personalization intelligence.

• Build a unified personalization intelligence capability across previously separate analytical disciplines.

• Build a unified personalization intelligence capability across previously separate analytical disciplines.
• Create a clear vision for how client intelligence evolves from primarily historical and batch-oriented analytics toward increasingly predictive, dynamic, and real-time insights.
• Establish standards and governance for personalization data, signals, analytics, and measurement.
• Build reusable analytical capabilities rather than one-off solutions for individual channels or use cases.
• Partner effectively across Product, Marketing, Technology, Enablement, Data, and Analytics organizations.
• Influence senior stakeholders across organizational boundaries without requiring ownership of downstream execution.
• Lead and develop a high-performing team spanning analytics, data science, decision science, client insights, and measurement.

• Senior leadership experience across personalization, client analytics, decision science, data science, marketing analytics, or related disciplines.
• Demonstrated experience building scalable analytics and personalization capabilities.
• Deep expertise in areas such as segmentation, behavioral signals, predictive modeling, lifetime value, experimentation, incrementality, and measurement.
• Experience applying advanced analytics, machine learning, and AI to client intelligence and personalization.
• Experience partnering with Technology teams to operationalize analytical capabilities and move toward real-time use cases.
• Strong track record working across Product, Marketing, Data, Analytics, and Technology organizations.
• Ability to translate complex analytical capabilities into clear business strategies and influence senior stakeholders.