Job Description

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.


We are currently seeking an Applied ML AI Executive Director as the

Head of Credit Card Collections Risk Modeling

team

.

In this critical role you will be managing a team of applied machine learning modelers in multiple working locations who are responsible for developing and maintaining best-in-class credit risk models catering to the collections and recovery functions within Chase Card Services. You will be responsible for identifying business opportunities for applying suitable machine learning algorithms to develop ML models that enhance the effectiveness of credit loss control. Your expertise and thought leadership in big data platforms (Hadoop/Cloud) and advanced ML techniques, such as deep learning, reinforcement learning and graph ML will substantially influence the direction of the next generation of risk models.


In this highly visible role, the successful candidate will demonstrate analytic leadership through business acumen, collaboration, and effective communication skills with senior management. Success in this role requires a strong foundation in machine learning and artificial intelligence, along with deep understanding of credit risk management. The candidate should have a proven ability to manage end-end ML/AI solutions, especially deploy ML models harnessing vast amounts of data and computation into distributed systems.


Job responsibilities:


Collaborate with risk strategy teams and operations to understand business needs, data generating process, system capability, and potential model impact. Design machine learning solutions to address business needs, including explainable machine learning models and reinforcement learning models Manage multiple model development projects Collaborate with various partners in Marketing, Finance, Technology, Model Governance, Compliance, Risk, Legal, etc. throughout the entire modeling lifecycle. Manage model risk and related governance and controls Synthesize the findings at various points through the model development process to share actionable insights with senior leadership and other stakeholders Drive constant innovations to drive sustained improvement in collections and recovery capabilities of the firm
Required qualifications, capabilities, and skills:


Ph.D. or MS degree in Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields Minimal 10-year of experience in developing and managing ML or predictive risk models in financial institutions Hand-on experience in developing and deploying real-time transaction models with massive data from various sources, internally and externally Developed ML/AI models in big data platform (Hadoop and Cloud) and deployed them into real-time scoring engines, such as mainframe, cloud or distributed computing systems Experience in developing and deploying commercial applications for machine learning that are interpretable Experience in open source programming languages for large scale data analysis such as Python / Scala / Java / PySpark Experience with supervised and unsupervised machine learning algorithms such as XGBoost, CNN, RNN, SVM, Reinforcement Learning, Markov Process Minimal 3-year experience managing a sizable team of data scientists/ modelers/ machine learning engineers Experience in managing a team in a dynamic environment of high mobility Polished and clear communications with senior management * Proven leadership in client/stakeholder/partner relationship management and high-performance team development