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 Senior Associate

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) 

Basic Qualifications 

Ph.D. or MS degree in 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 

6+ 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 4+ 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