Join a team where you will remain deeply technical and hands-on while elevating the teams delivery velocity, modeling craft, and engineering rigor.

As an AI Modeling Lead, within our Connected Commerce and Card organization, you will collaborate with colleagues across JPMorgan Chase to create high-impact quantitative models for our customers financial needs, specifically, to Connected Commerce & Cards.

Job Responsibilities: 

• Design, train, and deploy machine learning and AI models tailored to business goals, balancing performance metrics while keeping speed to market and complexity considerations in mind, with a clear focus on commerce use cases and measurable customer and business impact. 

• Lead modeling or data science engagements end-to-end, including interfacing with business, governance, and technology stakeholders; articulating clear business use cases; driving model development and deployment; and monitoring performance, with ownership of production readiness and operational reliability. 

• Be a subject matter expert and trusted advisor to business partners, helping them understand the strengths and limitations of our models. 

• Partner with Governance teams to ensure comprehensive model documentation, track performance metrics, and maintain adherence to regulatory compliance standards. 

• Coach and mentor junior team members, supporting their development in technical, business, and communication skills, and actively setting a culture where hands-on coding, strong reviews, and shipped outcomes are the standard. 

Required Qualifications, Capabilities, and Skills:

• M.S. degree in quantitative discipline (e.g., Machine Learning/AI, Computer Science, Engineering, Mathematics/Statistics, or Operations Research). 

• 57 years of hands-on experience developing machine learning (ML) models, with meaningful exposure to deep learning algorithms or advanced techniques such as reinforcement learning (RL) or optimization algorithms. 

• Proficient in Python with hands-on experience in machine learning and deep learning frameworks (TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Strong working knowledge of Jupyter Notebook/Lab is essential. 

• Demonstrated fluency with modern AI tooling, including LLMs, generative AI patterns, and emerging agentic frameworks, with the practical judgment to select tools that improve speed-to-value without compromising safety, controls, or maintainability. 

Preferred Qualifications, Capabilities, and Skills:

• PhD in quantitative discipline. 

• Experience developing advanced AI or ML models in consumer finance, financial services, tech, or a major retailer, with a track record of shipping at pace while maintaining strong technical and risk standards. 

• Knowledge of working on Databricks and Snowflake, and comfort operating in modern, collaborative engineering workflows (version control, testing discipline, and production deployment practices). 

• Contributions to the broader AI/ML community such as published research, open-source contributions, or conference presentations are a strong plus.