JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.5 trillion and 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 www.jpmorganchase.com.
Duties: Perform data extraction from different databases and different tables, summarize data and perform data quality check. Participate in design, development, implementation, and validation of CCAR model, Risk model, Collection model, Portfolio Marketing model, Acquisition Response model, and Balance Transfer Models using Linear Regression, Logistic Regression, Time Series, etc., and machine learning modeling techniques like Gradient Boost, Random Forest, Neural Network, etc. Conduct segmentation analysis for model development using Decision Tree, Machine Learning Classification technique, K-Means Clustering. Perform variable reduction using correlation, variable clustering, multi-variate analysis, etc. Perform ad-hoc statistical analysis for risk management and P&L related analysis. Understand Chase Credit Card business. Present models and analysis results, insight, recommendation to senior management. Work with business partner to implement the models into system for Risk Management and Marketing execution.
Minimum education required: Master’s degree or equivalent in Statistics, Mathematics, Economics, Engineering (Any), Physics, Operations, or related field.
Minimum experience required: 1 year of experience in statistical modeling, or related experience.
Skills required: Experience in predictive modeling and machine learning coupled with experience in working with large datasets. Experience developing or applying mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to discover useful information. Experience analyzing and interpreting big data and its impact in both operational and financial areas following comprehensive risk principles and procedures. Experience utilizing machine learning techniques, including Text Mining and Supervised and Un-Supervised Classification method, Gradient Boost, Random Forest and Neural Network. Experience performing data extraction, sampling, and statistical analyses using logistic regression, multinomial regression, multivariate analysis, discriminant analysis, neural network, principal components analysis, time series analysis, and panel data analysis. Experience partnering with business units in making strategic choices and investment decisions. Experience communicating opportunities, financial and process trade-offs from advanced statistical methods to senior leaders. Experience in SAS, R, and Python programming language and UNIX operating system. Experience with MS Office suite, including MS Excel Pivot tables and charts, VLookup, and MS PowerPoint. Employer will accept any amount of professional experience with the required skills.
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