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CCB - Risk-Fraud Machine Learning Data Scientist/Modeler-Vice President

Req #: 170119253
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Job Description:

CCB- Risk-Fraud- Machine Learning Data Scientist Modeler-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

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) 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 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 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 services).


Working for one of the largest banks, card issuers, and payments processors in the US, you will be protecting consumers and small businesses from financial fraud, including account takeovers and identity theft, as well as disrupting organized crime 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 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.


You will work in an industrial R&D/skunkworks environment, developing innovative predictive models on a dataset in the hundreds of TBs. 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, Operational Research, Physics, and other related quantitative fields
  • 1-3 experience in developing commercial applications for text mining and natural language processing 
  • 1-3 years’ experience in open source programming languages for large scale data analysis such as Python / Scala / Java
  • Experience in developing models using some (at least 3) of the following machine learning and optimization techniques like CNN, RNN, SVM, Reinforcement Learning, Markov Process for a commercial purpose
  • Ph.D. Mathematics, Statistics, Economics, Computer Science, Operational Research, Physics, or related field
  • Secured patents in area of speech recognition and / or speaker identification
  • Academic papers published in the area of machine learning in the top machine learning journals
  • Experience with implementing scalable machine learning/data mining algorithms making use of distributed/parallel processing
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