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CCB - Risk-Fraud Data Scientist/Modeler-Computer Vision-VP

Req #: 170119251
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Job Description:

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 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.  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 services).


 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.


Particular to this role, we are seeking a motivated individual that specializes in computer vision, including such specialties as handwriting recognition, signature verification, image recognition and classification, and/or facial recognition. The individual is expected to develop and implement algorithms that can provide non-intrusive and more accurate methods of customer identification and transaction authentication.

  • 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 computer vision, including such specialties as handwriting recognition, signature verification, image recognition and classification, and/or facial recognition 
  • 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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