Wholesale Credit Risk Portfolio Analytics- Vice President

Description 

Wholesale Credit Portfolio Analytics is tasked with deploying innovative methodologies using data 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. This role will be part of the Wholesale Credit Risk – Portfolio Strategic Analytics team that has been tasked to develop value-added risk analytics solutions through the deployment of advanced analytical frameworks and Machine Learning algorithms on top of the firm’s big data resources. In particular, the role will focus on leveraging data and advanced ML methodologies (including LLM and NLP techniques) to enhance the current End-to-End credit risk process across all of Wholesale.  

 Responsibilities

• Have deep understanding in modern Machine Learning methodologies, LLM and NLP techniques, and apply thoughtful quantitative, data science and analytical skills to solve complex business problems.
• Develop risk strategies that improve risk monitoring capabilities through using various source.
• Analyze structured/unstructured data from internal and external data sources to drive actionable insights in credit risk.  
• Lead development of event-driven scenario analytics to analyze the impact of 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. 
• Excellent problem solving, communications, and teamwork skills.
• Financial service background or Credit Risk experience preferred, but not required.
• Desire to use modern technologies as a disruptive influence within Banking.

JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.