Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.
 

As Wholesale Credit Portfolio Analytics Analyst in Wholesale Credit Portfolio Analytics team, you will be responsible for creating valuable risk analytics solutions using advanced analytical frameworks and the firm's big data resources. The focus will be on leveraging data to improve the End-to-End credit risk process across the wholesale portfolio. Additionally, the role involves clearly and concisely communicating findings and insights to stakeholders.

As part of Risk Management, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

 

 Job Responsibilities 

Develop and maintain credit risk rating methodologies, tools, and frameworks to improve risk management processes, including counterparty rating models, exposure management, and credit approvals. Collaborate with internal model review and controls teams to ensure new methodologies are approved and compliant. Use data science techniques to derive insights and communicate findings. Contribute to generating new ideas to address both ad hoc and strategic projects. Present findings and recommendations to senior management through presentations

Required Qualifications, Skills and Capabilities 

Relevant analytics, model/methodology development or credit risk experience Self-starter with creative problem-solving skills. Ideal candidates have experience in quantitative method development and data analysis and are comfortable discovering and communicating ideas through data. Degree in analytical field preferred (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics) Experience with modern analytic and data tools, particularly Python/Anaconda and/or R, Tensorflow and/or Keras/PyTorch, Spark, or SQL. 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