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Machine Learning Engineer (Associate) - Intelligent Solutions

Req #: 170081680
Location: New York, NY, US
Job Category: Technology
Potential Referral Amount: 0 US Dollar (USD)

JP Morgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. The firm is a leader in investment banking, financial services for consumers, small business and commercial banking, financial transaction processing, asset management, and private equity.

 

JP Morgan Intelligent Solutions (JPMIS) is the group considering ways to transform our data assets into opportunities for JPMorgan Chase by leveraging the vast amount of proprietary data across the firm. Protecting and managing intellectual property effectively as well as utilizing it to develop solutions that are both customized and scalable will enable the firm to create value for our shareholders.

 

The Machine Learning team at JPMorgan Chase combines cutting edge machine learning techniques with the company’s unique data assets to optimize all the business decisions we make. In this role, you will be part of our world-class machine learning team, and advance the state-of-the-art in financial applications ranging from pricing and credit models to natural language processing. Our work spans the company’s lines of business, with exceptional opportunities in each.

 

The successful candidate will apply sophisticated machine learning methods to banking applications including risk assessment, trading models, customer relationship management, and pricing models. Machine learning techniques will include feed-forward, recurrent, recursive and covolutional neural networks, maximum entropy models, and other algorithms related to time series analysis and supervised learning.

  ·         Expertise in linear algebra and calculus is required.

·         Familiarity with toolkits such as Torch, Theano, TensorFlow, Chainer and others is desirable.

·         A bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics or related field is required. (Graduate degree or PhD. preferred)

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