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CCB - Risk-Core Modeling- Card CCAR/CECL-Associate

Req #: 170038018
Location: Wilmington, DE, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 0 US Dollar (USD)
JPMorgan Chase (NYSE: JPM) is one of the oldest financial institutions in the United States, with a history dating back over 200 years. We are a leading global financial services firm with assets of $2.4 trillion. We operate in more than 100 countries with over 235,000 employees. We serve millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients.  We are a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing and asset management.  Our stock is a component of the Dow Jones Industrial Average.
Chase Consumer & Community Banking serves nearly 66 million consumers and 4 million small businesses with a broad range of financial services, including personal banking, small business banking and lending, mortgages, credit cards, payments, auto finance and investment advice. Consumer & Community Banking Risk Management partners with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Types of risk that occur in consumer businesses include fraud, reputation, operational, credit, market and regulatory, among others.
 
CCB Risk Core Card Modeler-CCAR-Associate
 
  • Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to discover useful information.
  • Design, develop, implement and validate statistical models and segmentation strategies for bank’s card risk, Comprehensive Capital Analysis and Review, loss reserve, risk appetite, and budget process.
  • Conduct complex risk analysis to provide management with business insights, recommendations of strategies and business actions for profitable growth opportunities, consumer credit quality and behavior trends, desired risk/return relationships and portfolio performance.
  • Partner with business units in making strategic choices and investment decisions. Communicate opportunities, financial and process trade-offs from advanced statistical methods to senior leaders.
  • Utilize graduate-level research and analytical skills to perform data extraction, sampling, and statistical analyses using logistic regression, multinomial regression, multivariate analysis, discriminant analysis, neural network, principal components analysis, time series analysis, panel data analysis and etc.
  • Analyze and interpret big data and its impact in both operational and financial areas following comprehensive risk principles and procedures.
  • PhD or  Master’s degree in Statistics, Econometrics, Operations Research, Mathematics or related quantitative field.  Education or experience must include:
  • At least 12 months of hands on work and research experience of advanced analytical skills in the areas of statistics, economic/econometrics modeling and data mining applicable to risk management in banking industry;
  • At least 2 years of experience  in programming languages and statistical software including SAS, SQL and STATA;
  • At least 12 months of experience in manipulating and analyzing big data and structuring large and multiple database systems including Oracle/Teradata /MySQL /SQLServer;
  • At least 2 years of experience of analytical and modeling methods including logistic regression, multinomial regression, multivariate analysis, discriminant analysis, neural network, principal components analysis, time series analysis, and panel data analysis;
  • Ability to clearly present analytical findings and methodology to both technical and non-technical audiences and make business recommendations via the use of Microsoft Word and PowerPoint;
  • Experience using formulas, and VBA macro in MS Excel.
    • Interest in computational statistics, in particular, the applications of machine learning and natural language processing to behavioral finance.
    • A strong understanding of machine learning theory.
    • Programming experience in R, python, Java ML packages and statistics.
    • Experience or knowledge of open source data science tool kits and technologies
    • Experience or knowledge of distributed computing such as Apache Spark or Tensorflow
    • Experience or knowledge of leading big data technologies like, Spark, Hadoop, etc.
 
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