Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.

As a Risk Modeling Machine Learning Associate in our Risk Modeling team, you will have the opportunity to develop and implement machine learning models, statistical models, segmentations, and strategies. You will leverage big data and distributed computing platforms, with applications in risk management for our credit card and small business portfolio. In this role, you will promote long term profitable growth, collaborate in a team environment, and effectively communicate results to senior management. Our Firmwide Risk Function is focused on cultivating a stronger, unified culture that embraces a sense of personal accountability for developing the highest corporate standards in governance and controls across the firm. Business priorities are built around the need to strengthen and guard the firm from the many risks we face, financial rigor, risk discipline, fostering a transparent culture and doing the right thing in every situation. We are equally focused on nurturing talent, respecting the diverse experiences that our team of Risk professionals bring and embracing an inclusive environment. Chase Consumer and Community Banking serves consumers and small businesses with a broad range of financial services, including personal banking, small business banking and lending, mortgages, credit cards, payments, auto finance and investment advice. Consumer and Community Banking Risk Management partners with each Consumer and Community Banking sub-line of business to identify, assess, prioritize and remediate risk. Types of risk that occur in consumer businesses include fraud, reputation, operational, credit, market and regulatory, among others.

• Utilize novel 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 to cater business need and deliver them into production.
• Collaborate with various partners in marketing, risk, technology, model governance, fair lending etc. through the modeling lifecycle (conceptualization, development, review, deployment, model usages and model maintenance)

• Advance degree in Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields
• Minimum 6 years of experience in developing and managing predictive risk models in financial industry.
• Minimum 4 years of experience and proficiency in coding (Python, Tensorflow or PyTorch, PySpark, SQL), familiarity with cloud services (AWS Sagemaker, Amazon EMR)
• Fundamental understanding of consumer lending business.
• Deep understanding of advanced machine learning algorithms (e.g. random forest, XGBoost, Neural Networks etc) as well as design and tuning procedures
• Effective communication

• Demonstrated experience in designing, building, and deploying production quality machine learning models. Experience in interpreting deep learning models is a plus.
• 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 skills, proven experience of implementing models in production.
• Ability to collaborate with multiple stakeholders on projects of strategic importance.