Applied AI ML VP

Job summary

Company Chase & Co. (NYSE: JPM) is a leading global financial services firm with operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, Company Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its Company and Chase brands. Information about Company Chase & Co. is available at Company website.

Chase Consumer & Community Banking (CCB) serves consumers and small businesses with a broad range of financial services. CCB Risk Management partners with each CCB sub-line of business to identify, assess, prioritize, and remediate risk.

CCB Risk Modeling - Applied AI ML VP

• Utilize cutting-edge approaches to design and develop sophisticated machine learning models to drive impactful decisions for the business
• Leverage big data/distributed computing/cloud computing platforms to optimize and accelerate model development processes
• Work closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into production
• Collaborate with various partners in marketing, risk, technology, model governance, etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models)
• It's an individual contributor VP role with no people management

Basic Qualifications

• Ph.D. or MS degreein Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields
• Deep understanding of advanced machine learning algorithms (e.g., regressions, XGBoost, Deep Neural Network - CNN, RNN and Transformer, Clustering, Recommendation) as well as design and tuning procedures
• Polished and clear communication

Preferred Qualifications

• 8+ years of experience in developing and managing predictive risk models in financial industry
• Demonstrated experience in designing, building, and deploying production quality machine learning and deep learning models. Experience in interpreting deep learning models is a plus
• 5+ years of experience and proficiency in coding (Python, Tensorflow or PyTorch, PySpark, SQL), familiarity with cloud services (AWS Sagemaker, Amazon EMR)
• Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is a plus
• Strong ownership and execution, proven experience in implementing models in production