As a Associate credit risk in Wholesale Credit Quantitative Research - Portfolio Analytics team, you will be part of the Wholesale Credit Risk Quantitative Research – Applied AI/ML team that has been tasked to develop Generative AI solutions on top of the firm’s big data resources. In particular, the role will focus on building tools based on LLM to enhance the current End-to-End credit risk process across all of Wholesale.   is tasked with deploying innovative methodologies using Generative AI in powerful new ways, and continue to expand the firm’s global presence. We aim to improve both our effectiveness and efficiency in managing wholesale credit risk across Commercial Banking and Corporate & Investment Banking. Commercial Banking (CB) serves over 19,000 clients nationally, including corporations, municipalities, financial institutions and not-for-profit entities, with annual revenue generally ranging from $20 million to $2 billion, and over 37,000 real estate clients, owners and investors. J.P. Morgan’s Corporate & Investment Bank (CIB) is a global leader across banking, markets and investor services. The world’s most important corporations, governments, and institutions entrust us with their business in more than 100 countries. 

Job responsibilities 

Have deep understanding in modern Machine Learning methodologies, LLM and NLP techniques, and apply thoughtful data science and analytical skills to solve complex business problems. Develop risk strategies that improve risk monitoring capabilities through the use of data from various source. Analyze structured/unstructured data from internal and external data sources to drive actionable insights in credit risk.   Lead development and rapid deployment AI solutions based on macro-economic factors and current events on the Wholesale portfolio. Develop data visualization and summarization techniques to convey key findings in dashboards and presentations to senior management.

Required qualifications, capabilities, and skills :

Advanced degree in analytical field (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics) Deep understanding and practical expertise and/or work experience with Machine Learning. LLM/NLP expertise or experience is strongly preferred  Experience across broad range of modern analytic and data tools, particularly Python/Anaconda, Tensorflow and/or Keras/PyTorch, Spark, SQL etc. Experience working on Cloud is preferred Experience with model implementation/production deployment is preferred Excellent problem solving, communications, and teamwork skills Financial service background preferred, but not required Desire to use modern technologies as a disruptive influence within Banking