Risk Modeling - Machine Learning Associate

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 within the Risk Modeling team, you will be responsible for the development and implementation of machine learning models, statistical models, segmentations, and strategies. Utilizing big data and distributed computing platforms, your work will be applied to risk management for our credit card and small business portfolio. You will be instrumental in promoting long-term profitable growth through your strong business acumen. Working in a collaborative team environment, you will effectively communicate results to senior management. 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.

Job responsibilities

• 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) 

Required qualifications, capabilities, and skills

• 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 

Preferred qualifications, capabilities, and skills

• 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.