CCB - Risk-CECL-Card Modeler - Vice President
Req #: 170071489_1
Job Category: Accounting/Finance/Audit/Risk
CCB Risk Core Modeling/Data Scientist- Card- Vice President
JPMorgan 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, JPMorgan 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 J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at http://www.jpmorganchase.com/.
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 brings and embracing an inclusive environment.
Chase Consumer & Community Banking (CCB) 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.
The CCB Risk Core Modeling Data Scientist in Chase credit card modeling team will be responsible for end-to-end management of complex model development and ad-hoc analytic projects to drive innovation and research new opportunities for revenue growth or risk mitigation. Success in this role requires a strong foundation in predictive modeling and machine learning coupled with experience in working with large dataset. In this highly visible role, the successful candidate will be able to think like an analytic leader with strong business acumen, collaborate in a team environment and communicate the business analytics and model statistics/insights succinctly to senior management.
Your key responsibilities will include:
Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to discover useful information.
Analyze and interpret big data and its impact in both operational and financial areas following comprehensive risk principles and procedures.
Feature engineering and feature selection for traditional GLM models and machine learning models
Design, develop, implement and validate statistical models and segmentation strategies for bank’s card risk, marketing, collection, and /or Comprehensive Capital Analysis and Review processes, as needed. Utilize graduate-level research and analytical skills to perform data extraction, sampling, and statistical analyses using logistic regression, multinomial regression, multivariate analysis, discriminant analysis, neural network, principal components analysis, time series analysis, panel data analysis and etc.
Conduct complex risk analysis to provide management with business insights, recommendations of strategies and business actions for profitable growth opportunities, consumer credit quality and behavior trends, desired risk/return relationships and portfolio performance.
Partner with business units in making strategic choices and investment decisions. Communicate opportunities, financial and process trade-offs from advanced statistical methods to senior leaders.
Minimum of 7 years of hands on work and research experience of advanced analytical skills in the areas of statistical modeling and data mining
Master's degree in Mathematics, Statistics, Computer Science, or related fields
Expert in generalized linear models, unsupervised and supervised machine learning algorithms
Demonstrated experience with Big Data tools like Hadoop & Spark
Demonstrated proficiency in advanced analytical languages such as R, Python, Scala, SAS
Experience with traditional database/system languages (e.g. SAS, SQL, etc.) to collaborate with other data analysts/systems
Ph.D. Mathematics, Statistics, Computer Science, or related field
Prior experience of data analytics and model development in financial Industry