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Bank of America

Runs a US consumer bank, brokerage and investment bank

Head of Personalization Intelligence

Data ScienceConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • 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 segmentation, behavioral signals, predictive modeling, lifetime value, experimentation, incrementality, and measurement.
  • Experience applying advanced analytics, machine learning, and artificial intelligence 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.

Responsibilities

  • 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.
  • 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.
  • 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.
  • Bring together client value, needs, behaviors, and signals to provide a richer understanding of 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.
  • 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 so it 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 to continuously improve the underlying personalization intelligence, not simply to report performance.
  • Build a unified personalization intelligence capability across previously separate analytical disciplines.
  • Create a clear vision for evolving client intelligence 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.
  • Advance the personalization intelligence layer so client understanding becomes richer, signals become timelier, insights become more actionable, and measurement continuously improves intelligence for future interactions.

About the Company

Bank of America

Runs a US consumer bank, brokerage and investment bank

Bank of America runs one of the largest US banks, serving consumers, small businesses, wealthy families, corporations and institutional investors. Alongside checking accounts, credit cards, mortgages and business loans, it owns the Merrill brokerage, a private bank for wealthy clients and a global investment bank that advises on deals and trades stocks and bonds. Customers reach it through thousands of branches and ATMs and its mobile banking app. It earns money mainly from the gap between what it charges on loans and pays on deposits, plus fees from advice, asset management, deal work and trading. It grew from a San Francisco bank founded in 1904 and is headquartered in Charlotte, North Carolina.

Company size
10,001+
Company stage
IPO
Headquarters
Charlotte, NC
Founded
1904

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