Marketing Data & Agentic AI Transformation Lead

JPMorgan Chase & Co.•Columbus, OH

About The Position

As a Decision Scientist Lead on Transformation Analytics with the Transformation Analytics Team, you will own the foundational data strategy for CSP-21 (Marketing Process Automation)—our program to modernize end-to-end marketing through Agentic AI, integrated tools, and automation. You will be accountable for how marketing data is designed, produced, governed, and used across data architecture, marketing execution, customer onboarding, and response analytics. Rather than running day-to-day campaign measurement, you will transform it—building AI-enabled, standardized, self-serve measurement and analytics that reduce manual effort, improve consistency, and scale across campaigns and channels. To be successful, you'll need to influence and impact a variety of functions including marketing analytics and execution, customer onboarding, data governance, data architecture, and design. You will set and enforce data governance (standard definitions, quality controls, and lineage) so results are consistent and trusted. You will partner across Marketing, Product, Data, and Technology to deliver centralized, durable solutions that accelerate campaign delivery and optimization.

Requirements

Master's degree in a related field or bachelor's with 5+ years’ relevant experience in Marketing Campaign Development and Analytics (Marketing Life Cycle)
Familiarity with Agentic AI tools (agent orchestration, function calling, prompt design, human-in-the-loop), applying them to enhance marketing operations, analytics, and decision-making
Ability to identify and secure data assets for audience creation, campaign execution, and analytics aligned with business goals
Experience delivering analytics and insights to management levels (including test design, financials, conversion reporting, response analysis, incremental analysis, profiling, segmentation)
Strategic business and tactical implementation background
5 + years Python, complex SQL with star Schema data models, SAS, R, and other data analysis languages and skilled in cloud ecosystems (AWS, GCP, Azure, Snowflake, Databricks)
Ability to define data and align access rolls to the correct level of security
Strong analytical and problem-solving abilities
Experienced with ETL/data wrangling tools (Alteryx, Trifacta, Pentaho, Altair) and knowledge of data warehouses and related concepts including architecture
Expertise in reporting applications (Tableau, PowerBI, Qlik)
Consulting ability to influence business partners and exceptional communication skills, able to translate complex information into clear presentations for senior leaders

Responsibilities

Lead end-to-end audience strategy and decision science by leveraging internal and external data to continuously refine targeting for Business Banking products and services.
Drive analytics optimization across the full campaign lifecycle, including opportunity sizing, targeting strategy, test-and-learn design, onboarding, response analytics, performance measurement, and delivery of clear, actionable insights and recommendations.
Own stakeholder engagement and operating cadence by managing the data portfolio, setting priorities, defining delivery plans, and providing strategic direction for decision science initiatives and experimentation.
Partner with marketing, product, data, and technology leaders to define requirements, shape roadmaps, and build scalable foundational data solutions and prototypes that accelerate time-to-impact and improve operational efficiency.
Serve as Strategic Data Owner Lead for critical CSP-21 marketing datasets and capabilities by defining the scope of the data domain, identifying essential data elements, and maintaining precise business definitions and metadata (such as KPI and audience descriptions).
Establish and oversee data quality standards—including completeness, accuracy, and timeliness—and implementing robust controls.
Manage data mapping and lineage documentation to ensure comprehensive traceability from upstream sources through all downstream transformations and outputs.
Inform Architecture and Design to cover all aspects of Marketing, Product and Sales life cycles including completing architectural diagrams for intake into platforms.
Conducting complex data discovery and research to inform sourcing decisions, ensuring the data's suitability for campaign deployment, measurement, and experimentation.
Develop foundational data prototypes designed to facilitate campaign acceleration, which will subsequently be formalized into requirements for Data Delivery software engineering teams.