Job Description

Job summary:

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 & 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 & Community Banking Risk Management

partners with each CCB 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.


Join our

Model Insights Team

, a Center of Excellence within Consumer & Community Banking (CCB) Risk Modeling, committed to tracking of comprehensive health of machine learning models. We are responsible for sanity of model inputs and score performance tracking for CCB risk decision models. Team collaborates with model developers to identify and recommend potential opportunities for model calibration. We are constantly seeking for opportunities to enhance model performance tracking framework, with aim of providing feedback loop to risk strategies. We are seeking candidates who possess extensive knowledge of data science techniques, appreciation for data combined with of domain expertise, and a keen eye for detail and logic. It's an opportunity to make an impact to model performance monitoring and governance practices for CCB risk models.

Job responsibilities:

Drive synergy in model performance tracking across different sub-lines of business. Enhance model performance framework to holistically capture model health, providing actionable insights to model users. Collaborate with model developers to identify potential opportunities for model calibration and conduct preliminary Root Cause Analysis in case of model performance decay. Design and build robust framework to monitor quality of model inputs. Explore opportunities to drive efficiency in model inputs and performance tracking through use of Large Language Model (LLM). Partner with teams across, Risk, Technology, Data Governance, and Control to support effective model performance management and insights. Deliver regular updates on model health to senior leadership of risk organization and the first line of defense.

Required qualifications, capabilities, and skills

Advanced degree in Mathematics, Statistics, Computer Science, Operations Research, Econometrics, Physics, or a related quantitative field. Minimum of 3 years of experience in developing and managing predictive risk models in financial industry. Proficiency in programming languages such as Python, PySpark, and SQL, along with familiarity with cloud services like AWS SageMaker and Amazon EMR. Deep understanding of advanced machine learning algorithms (e.g. Decision Trees, Random Forest, XGBoost, Neural Networks, Clustering etc) Strong conceptual understanding of performance metrics used to monitor health of machine learning models. Fundamental understanding of the consumer lending business and risk management practices. Experience of working with large datasets with strong ability to analyze, interpret, and derive insights from data. Advanced problem-solving and analytical skills, with a keen attention to detail. Excellent communication skills, with the ability to convey complex information clearly and effectively to senior management.

Preferred qualifications, capabilities, and skills

Experience of data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). Proven expertise in designing, building, and deploying production-quality machine learning models. Ability to effectively collaborate with multiple stakeholders on projects of strategic importance, ensuring alignment and successful outcomes. * Basic level of proficiency in Tableau