JPMorgan Chase CCB - Risk-Fraud Data Scientist/Modeler-ML-Associate in New York, New York
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 Machine Learning group within the CCB Risk Fraud Modeling team is responsible for developing and implementing best-in-class fraud prevention and detection models and analytical tools. The team provides diverse models and analytical tools used to identify potentially fraudulent transactions across different lines of business (card, retail, auto, merchant services).
Working for one of the largest banks, card issuers, and payments processors in the US, you will be fighting crime and protecting consumers and small businesses from financial fraud, including account takeovers and identity theft, with mathematical modeling.
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 billions of 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.
You will work in an industrial R&D/skunkworks environment, developing innovative predictive models on a dataset in the hundreds of TBs and higher. As there are no known model architectures that are effective on fraud datasets in general, you will need to develop them.
Master's degree in Mathematics, Statistics, Economics, Computer Science, Operations Research, Physics, and other related quantitative fields
At least 1 years’ experience with data analysis in Python
Experience in designing models for a commercial purpose using some (at least 3) of the following machine learning and optimization techniques: CNN, RNN, SVM, Reinforcement Learning, Random Forest/GBM
A strong interest in how models work, the reasons why particular models work or not work on particular problems, and the practical aspects of how new models are designed
PhD in a quantitative field with publications in top journals, preferably in machine learning
Experience with model design in a big data environment making use of distributed/parallel processing via Hadoop, particularly Spark and Hive
Experience designing models with Keras/TensorFlow on GPU-accelerated hardware
JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran.