Our Branch Network Modeling team develops advanced analytics and machine learning solutions that inform high-impact decisions across physical location strategy and field workforce effectiveness.
As an Applied AI Modeling Vice President in Branch Network Modeling team, you will build advanced artificial intelligence (AI) and machine learning (ML) models that directly shape high-stakes decisions impacting Chase's branch network and the bankers who serve our customers. Your models will help optimize our branch network, using geospatial AI and graph-based models to determine where Chase should invest, grow, or reposition its physical footprint, or will empower our bankers in the field to serve our customers using techniques like reinforcement learning and behavioral science.
Job responsibilities
- Develop and launch AI and ML models that solve complex, ambiguous business problems in Consumer Banking, spanning areas such as retail network optimization, investment optimization, resource allocation, and sales effectiveness.
- Lead modeling engagements end-to-end, including interfacing with business, governance, UX, and technology stakeholders; articulating clear business use cases; delivering on project plans; and working with large, complex datasets — including geospatial, demographic, transactional, and behavioral data — to formulate testable business hypotheses.
- Translate technical model outputs into clear, actionable recommendations for non-technical business partners in Real Estate, Finance, and Market Strategy.
- Partner with governance teams to expedite fair and thorough model reviews, track performance metrics, and maintain adherence to regulatory compliance standards.
Required qualifications, capabilities, and skills
- Advanced degree (master's or PhD) in a quantitative or spatial discipline such as Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or Geography, or a related field.
- 4+ years of hands-on, relevant industry experience in developing and deploying AI/ML models, including statistical modeling, ML, reinforcement learning, or optimization algorithms.
- Proficient in Python with hands-on experience in ML and deep learning frameworks (TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Strong working knowledge of Jupyter Notebook/Lab and cloud computing.
- Geospatial analytics, spatial statistics, or spatial optimization
- Graph neural networks, network science, or graph-based optimization
- Reinforcement learning, multi-armed bandits, or online/continuous learning
- Behavioral modeling, adaptive intervention design, or human performance optimization
Preferred qualifications, capabilities, and skills
- Hold a PhD in a relevant discipline.