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 Executive Director in Branch Network Modeling team, you lead a team of AI/ML scientists who build advanced geospatial, graph-based, and network optimization models that directly shape Chase's branch network strategy, including where to open, relocate, or reformat branches. You will be the face of your team's models in high-stakes conversations with senior leaders across Real Estate, Finance, and Market Strategy, as well as with Governance and Compliance stakeholders. Your success will be measured not only by the technical sophistication of your models, but by whether those models earn the trust of the business and change how decisions are made.
Job responsibilities
- Build and lead a high-performing team of AI/ML scientists focused on geospatial and graph-based AI modeling in support of Chase's branch network — providing development plans, structured growth opportunities, and visibility to senior stakeholders that position your team members for long-term career success.
- Partner with Real Estate, Finance, and Market Strategy stakeholders to understand their decision-making processes, break down silos between technical and business teams, and enable them to draw maximum benefit from AI/ML models.
- Partner closely with Product, Data, and Engineering teams to translate modeling innovations into scalable, production-ready solutions.
- Lead emerging AI talent in research and adoption of cutting-edge AI techniques, ensuring the right balance between methodological rigor, speed to market, and practical business impact.
- Establish best practices for geospatial model development and cloud-native deployment, ensuring compliance with governance and regulatory expectations.
- Oversee rigorous validation, documentation, and monitoring of models, proactively identifying risks and ensuring adherence to internal and external standards while coaching the team to carry these standards into future leadership role
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.
- 10+ years of hands-on experience in developing and deploying advanced AI/ML models, with 5+ years in a technical leadership or people management role.
- Documented expertise in one or more of the following: geospatial analytics and spatial statistics, graph neural networks and network science, optimization and operations research, or advanced machine learning methods applied to location strategy or network design problems.
- Exceptional communication skills and high emotional intelligence, with the ability to translate technical complexity into business language and earn stakeholders’ trust.
- Demonstrated leadership in building and mentoring high-performing technical teams.
- Rigorous statistical thinking, including the ability to interrogate a model's assumptions, defend its methodology under scrutiny from quantitative and non-quantitative audiences alike, and anticipate its behavior under different scenarios.
Preferred qualifications, capabilities, and skills
- Hold a PhD in a relevant discipline.
- Experience with geospatial tools and libraries (e.g., GeoPandas, PySAL, H3, Esri/ArcGIS) and/or graph ML frameworks (e.g., PyTorch Geometric, DGL, NetworkX).
- Experience with geospatial data and tooling including, but not limited to: ArcGIS, Carto, Wherobots, or QGIS.
- Experience with facility location problems, trade area modeling, network design, or spatial optimization in retail, logistics, real estate, or financial services.
- Contributions to applied research in geospatial AI, graph ML, network optimization, or spatial statistics, whether through publications in peer-reviewed journals, patents, or industry presentations.
- Background in financial services, consumer banking, or a regulated industry with model risk management expectations.
- Experience managing stakeholder relationships with non-technical senior leaders in functions such as Real Estate, Finance, or Strategy.
