CCB-Risk-Fraud Machine Learning Data Science Modeler - VIce President CCB-Risk-Fraud Machine Learning Data Science  …

J.P.Morgan
in New York, NY, United States
Permanent, Full time
Be the first to apply
Competitive
J.P.Morgan
in New York, NY, United States
Permanent, Full time
Be the first to apply
Competitive
CCB-Risk-Fraud Machine Learning Data Science Modeler - VIce President

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

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. You will work in an industrial R&D/skunkworks environment, developing innovative predictive models on a dataset in the hundreds of TBs.

In this role, you will lead a small team of elite analytical experts to identify and retool 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. In addition, you will work with technology partners to develop and improve cutting-edge real-time and batch model execution environments that process hundreds of millions of transactions per day. You will coordinate the design, development, deployment and monitoring of production real-time machine learning systems that execute on many millions of transactions per day and impact up to half the households in the USA.








Qualifications
  • Master's degree in Mathematics, Statistics, Economics, ComputerScience, Operations Research, Physics, and other related quantitative fields
  • At least 5 years' experience with design and deployment of liveproduction models implemented in Python with modern data science techniquessuch as neural networks or gradient-boosted trees
  • An expert who knows how models work, the reasons why particular modelswork or not work on particular problems, and the practical aspects of how newmodels are designed
  • Proven leadership in driving changes/delivering values and smallteam management experience
  • Proven ability to coordinate the work of a data science team withtheir technology, business and risk management partners
    Preferred
  • PhD in a quantitative field with publications in top journals,preferably in machine learning
  • Hand-on experience working with bare-metal hardware in a Linuxshell
  • Experience with model design in a big data environment via Hadoop,Spark and Hive
  • Experience designing models with Keras/TensorFlow, PyTorch, orother frameworks on GPU hardware
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