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CCB - Risk-Fraud Sr. Data Scientist/Machine Learning-ED

Req #: 170077696
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 5000 US Dollar (USD)
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 Fraud- Senior Data Scientist/Machine Learning Director will be managing a team of data scientists in the Fraud Modeling team and will be 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 responsible for the development, implementation, documentation, and ongoing monitoring of several machine learning models in various stages of the life cycle at any given time.
You will be charged with identifying business opportunities for applying suitable machine learning algorithms to develop predictive models that enhance the effectiveness of fraud loss control. Your expertise and thought leadership in big data platforms and machine learning techniques will substantially influence the direction of the next generation fraud detection models.
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 in real time. Expert communication and collaboration skills are also required. Your key responsibilities include: 
  • 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 solutions to address business needs
  • Manage multiple model development projects
  • Collaborate with Information Technology for model deployment
  • Manage model risk and related governance and controls
  • 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 machine learning 
  • 1-3 years of experience in open source programming languages for large scale data analysis such as Python / Scala / Java
  • 1-3 years of experience in managing a team of data scientists
  • 5+ years of experience in developing or managing the development of predictive models
  • 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 machine learning
  • Academic papers published in the area of machine learning in the top machine learning journals
  • Experience in  implementing scalable machine learning/data mining algorithms making use of distributed/parallel computing systems
  • Experience in developing fraud detection/prevention models in financial industry
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