JPMorgan Chase CCB - Risk-Fraud Core Modeler-VP in Wilmington, Delaware
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 bring and embracing an inclusive environment
Chase Consumer & Community Banking (CCB) s erves 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 Fraud Modeling team is an analytical center of excellence to all fraud risk managers and operations across the bank. The team provides diverse models and analytical tools used to identify potentially fraudulent transactions across different lines of business (card, retail, auto, merchant service.
The CCB Risk Core Modeling Data Scientist will be a key individual contributor on the Fraud Modeling team that is responsible for developing and implementing best-in-class fraud prevention and detection models and analytical tools.
In this role, you will be the analytical expert for identifying and retooling suitable machine learning algorithms that can enhance the fraud risk ranking of particular transactions and/or applications for new products. This includes a balance of feature engineering, feature selection, and developing and training machine learning algorithms using cutting edge technology to extract predictive models/patterns from data gathered for hundreds of millions transactions. Your expertise and insights will help us effectively utilize big data platforms, data assets, and analytical capabilities to control fraud loss and improve customer experience.
Success in this role requires a strong foundation in predictive modeling and machine learning coupled with proven ability to deploy scalable solutions that can handle a massive amount of data and computation. Expert communication and collaboration skills are also required. Your key responsibilities will include:
Feature selection for traditional GLM models (e.g Lasso, ElasticNet, etc) and machine learning models
Machine Learning Algorithm Development
Retooling/enhancing existing machine learning algorithms
Implementing new machine learning algorithms that are available from the public domain
Fraud Detection Model Development
Collaborate with fraud prevention/detection strategy teams and operations to understand business needs, data generating process, system capability, and potential impact of models.
Design machine learning algorithms that can be used to improve the fraud prevention/detection scores
Source data and apply feature engineering for model development and deployment
Provide requirements and assist Information Technology for model deployment
Document model solutions and address questions/concerns from model risk and control partners
Master's degree in Mathematics, Statistics, Economics, 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, Economics, Computer Science, Operational Research, Physics, or related field
Experience with implementing scalable machine learning/data mining algorithms making use of distributed/parallel processing
Experience with model development in Financial Industry is preferred
JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran.