Applied AI/ML Modeling - Vice President
On-site · New York City, New York, United States
New York City, New York, United StatesOn-siteFull TimeSenior LevelMasters DegreeInvestment BankingEnterprise
Type
Full Time
Level
Senior Level
Education
Masters Degree
Company size
Enterprise
Industry
Investment Banking
Job Summary
Vice President, Applied AI/ML Modeling, leads end-to-end development of AI/ML models to optimize Chase’s branch network using geospatial and graph-based techniques; translates model outputs into actionable recommendations for non-technical partners; collaborates with governance for model reviews and regulatory compliance; requires an advanced degree and 4+ years in AI/ML, proficient in Python with TensorFlow/PyTorch and ML libraries; preferred PhD; located in New York, NY.
Required Qualifications
- 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.
- Deep expertise in 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; Experience with Databricks, Snowflake, or similar platforms.
- Hold a PhD in a relevant discipline (preferred).
- Experience developing advanced AI or ML models in consumer finance, logistics, major retailers, or AI-native platforms.
- Experience with geospatial tools and libraries (e.g., GeoPandas, PySAL, H3, Esri/ArcGIS, Carto, Wherobots, QGIS), graph ML frameworks (e.g., PyTorch 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.
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