Vice President Digital Marketing Analytics - Card Data Analytics

Lead digital marketing analytics research and experiments that turn data into measurable growth and customer outcomes.

You will help shape digital marketing strategy through rigorous analysis, experimentation, and clear storytelling that drives business outcomes. You will work with partners across Marketing, Finance, Product, and Technology to turn complex questions into measurable actions. You will grow and lead a high-performing analytics team, building scalable, reliable ways of working that improve how marketing decisions are made. As a Vice President, Digital Marketing Analytics at JPMorganChase within the Digital Marketing Analytics team, you will lead quantitative research and experimentation to optimize marketing performance and customer experiences. You will translate customer behavior and campaign performance into insights that leaders can act on, and you will build team capability through coaching, hiring, and a culture of scientific rigor and responsible use of advanced analytics. You will take a pragmatic approach to generative AI and large language models (LLMs) as complementary tools, while relying on classical statistical and causal methods for core measurement and optimization workstreams. Job responsibilities Deliver effective quantitative problem solving and analytical research to support key digital marketing initiatives Apply deep business understanding and advanced analytical techniques to drive research, experimentation, and measurable outcomes Lead, mentor, hire, and develop a high-performing analytics team; promote scientific rigor, ethical AI, and continuous learning Maintain a pragmatic approach to generative AI and large language models (LLMs) as complementary tools, prioritizing classical statistical methods for core measurement and optimization work Partner with cross-functional teams (for example, Marketing and Finance) to drive insights into action and deliver business impact Design and implement scalable, reliable analytics processes to optimize business outcomes Conduct extensive analysis of marketing performance, measurement configuration and settings, and customer behavior to improve channel strategies and optimization using advanced quantitative methods Own and support strategic measurement initiatives, including effectiveness evaluation and profit and loss analysis; quantify statistical and practical significance Solve unstructured business problems and develop deep-dive analyses of customer behavior using multiple analytics and statistical techniques Conduct hypothesis testing and advanced experimental design (A/B and multivariate tests) to measure the impact and effectiveness of marketing strategies Required qualifications, capabilities and skills Graduate or post-graduate degree in a quantitative discipline (for example, Computer Science, Statistics, Mathematics, Finance, Economics, Data Analytics, or Machine Learning) 5+ years of hands-on analytics experience in banking strategic analytics Hands-on proficiency with Python and SQL; experience with visualization tools (for example, Tableau) and analytics tools (for example, Alteryx) Experience with Adobe Analytics (implementation, reporting, and insight generation) Strong statistical or econometric foundation with hands-on experimentation and measurement experience, including A/B testing, causal inference, and experimentation frameworks Strong advanced analytics skills using SAS, Python, or R Excellent communication skills with the ability to translate complex models into clear explanations and reason codes, influencing cross-functional stakeholders and senior leadership Exposure to enterprise AI enablement, LLM-assisted workflows, or analytics transformation programs Ability to evaluate opportunities to apply AI, generative AI, and intelligent automation to improve investigative analysis, documentation, operating procedures, knowledge retrieval, issue summarization, and workflow efficiency Familiarity with supervised learning, anomaly detection, semi-supervised learning, clustering, feature stores, calibration and threshold optimization, and imbalanced learning Preferred qualifications, capabilities and skills Proficiency in big data extract, transform, and load processes across structured and unstructured data sources Professional experience with AWS, Spark or EMR, and Snowflake Experience with Confluence and generative AI tools (for example, ChatGPT), subject to firm-approved usage People leadership experience, including recruiting, coaching, performance management, and fostering an inclusive, high-accountability culture Strong understanding of IT processes and databases, with the ability to work directly with data owners and custodians