Join a team that powers JPMorgan Chase with insights to create competitive advantages for our business and deliver value for our customers and unifies data and analytics talent across the firm and encompass a variety of disciplines including data science, reporting, quantitative analytics, and data governance.
As a Machine Learning Lead, within the Branch Channel Analytics team, you will lead the development and deployment of machine learning solutions that power the next generation of banker-to-customer outreach moving beyond single-action recommendations toward a multi-action prioritization framework that helps bankers deliver the right message, to the right customer, at the right time.
Job Responsibilities:
- Design, develop, and deploy machine learning models that optimize proactive banker outreach, building and evolving a multi-action prioritization framework that recommends and ranks multiple sets of actions (e.g., product conversations, servicing follow-ups, relationship deepening) for bankers to execute across the branch network.
- Serve as a subject matter expert on a wide range of ML techniques and optimizations, including ranking/recommendation systems and multi-objective optimization approaches.
- Take full ownership of the entire code development lifecycle in Python, from proof of concept and experimentation to delivering production-ready solutions.
- Collaborate with cross-functional teams — including marketing, product, risk, and other Data & Analytics teams — to align model outputs with business goals
- Communicate complex analytical findings and model outputs in intuitive business language to senior stakeholders, translating business requests into analytical strategies.
Required Qualifications, Capabilities, and Skills
- Bachelor's degree with 7+ years of experience in a related discipline, or 5+ years and a Master's or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- At least 5 years of experience applying data science and ML techniques to solve business problems, with intermediate-to-advanced Python proficiency and 5+ years of SQL experience (Teradata preferred) required.
- 2+ years of measurement and optimization experience, with demonstrated expertise in ranking, recommendation, and/or multi-objective optimization systems and a strong grasp of the full test/learn cycle.
- Solid background in machine learning and deep learning methods, with understanding of frameworks such as PyTorch or TensorFlow.
- A unique combination of technical skills, storytelling ability, and business intuition — with the ability to translate complex analytical concepts into intuitive business language, influence cross-functional partners, and drive projects through to completion with limited supervision.
- High standards for work quality with meticulous attention to detail, ensuring accuracy and rigor across all analytical outputs and deliverables.
- In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
Preferred Qualifications, Capabilities, and Skills
- Experience implementing next-gen models using machine learning in a Hadoop environment (e.g., kNN, MDP, neural networks, ensemble methods).
- Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like SageMaker, EKS, etc..
- PySpark experience is a plus.
- Financial services experience is a plus, but not required.