Applied AI​/ML Modeling - Vice President

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-stakesdecisionsimpacting

Chase’s branch network and the bankers who serve our customers. Your modelswillhelp optimize our branch network,using geospatial AI and graph-based models todeterminewhere Chase should invest, grow, or reposition its physical footprint,orwillempower our bankers in the field to serve our customers using techniques like reinforcement learning and behavioral science.

• 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 toexpeditefair and thorough model reviews, track performance metrics, andmaintainadherence to regulatory compliance standards.

• 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 inMLanddeep learning frameworks (Tensor Flow,PyTorch) and libraries (e.g., Num Py, Scikit-Learn, Pandas). Strong working knowledge ofJupyter
  
  Notebook/Lab andcloud computing.
• Deepexpertisein at least one of the following, with meaningful exposure to at least one other:
  • 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

• Hold a PhD in a relevant discipline.
• Experience developing advanced AI or ML models in consumer finance,logistics, major retailers, or AI-native platforms.
• Experience with at least one of the following: geospatial tools and libraries(e.g.,Geo Pandas,PySAL, H3, Esri/ArcGIS, Carto,Wherobots, QGIS),graph ML frameworks(e.g.,Py Torch Geometric , DGL,NetworkX), RL libraries(e.g.,RLlib, Stable Baselines,Vowpal Wabbit).
• Familiarity with behavioral science concepts (e.g., nudge theory, decision theory) or experience building adaptive, continuous learning, or recommendation systems.
• Experience with Databricks,Snowflake, or similar platforms.