JPMorgan Chase
Applied AI/ML Modeling - Senior Associate
$114K–$195K
NY +1 · Senior · Full-time · Posted Oct 5, 2026
- Location
- NY
Columbus, OH - Experience
- 3+ years of relevant industry experience.
- Education
- 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.
About the job
Build and deploy AI/ML models to optimize Chase’s branch network and support field banker effectiveness, translating model results into business recommendations.
What you'll do
- Develop and launch models for retail network optimization, investment optimization, resource allocation, and sales effectiveness.
- Lead end-to-end modeling engagements, including stakeholder collaboration, hypothesis development, and work with complex datasets.
- Explain model recommendations to business partners and support governance reviews, performance tracking, and regulatory compliance.
What they ask for
- Advanced degree (master’s or PhD) in a quantitative or spatial discipline, or a related field.
- 3+ years of relevant industry experience developing and deploying AI/ML models.
- Proficiency in Python, ML/deep learning frameworks and libraries, Jupyter Notebook/Lab, and cloud computing.
- Expertise in at least one specified specialty area and meaningful exposure to at least one other.
Nice to have
- PhD in a relevant discipline; experience developing advanced AI/ML models in consumer finance, logistics, major retailers, or AI-native platforms.
- Experience with listed geospatial, graph ML, or RL tools; behavioral science or adaptive systems; or Databricks, Snowflake, or similar platforms.
Pay
$114,000.00-$195,000.00 USD base salary; $114,000.00-$175,000.00 USD base salary
Tools
Python, TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas, Jupyter Notebook/Lab, Databricks
AI focus
Develop AI/ML models, including geospatial AI, graph-based models, reinforcement learning, and behavioral modeling, for branch network decisions and field workforce effectiveness.
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