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 Fraud Modeling - Machine Learning Associate within our Consumer and Community Banking Risk Modeling team, you will be responsible for the development and implementation of machine learning models, statistical models, segmentations, and strategies. You will have the opportunity to utilize big data and distributed computing platforms, applying them to risk management for our consumer and small business portfolio. In this role, you will contribute to long-term profitable growth through strong business acumen, work collaboratively within a team environment, and effectively communicate results to senior management. This role provides an exciting opportunity to be part of a dynamic team and make a significant impact on our business. 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.

 

Job responsibilities

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) 

Required qualifications, capabilities, and skills

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, capabilities, and skills

Minimum 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 Minimum 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