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CCB Risk - Senior Data Scientist - Vice President

Req #: 180031654
Location: Columbus, OH, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 0 US Dollar (USD)
 
 
JP Morgan Chase
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with 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 http://www.jpmorganchase.com/.
 
Our Firmwide Risk Function
Our Firmwide Risk function is focused on cultivating a stronger, unified culture that embraces a sense of personal accountability for developing the highest corporate standards in governance and controls across the firm. Business priorities are built around the need to strengthen and guard the firm from the many risks we face, financial rigor, risk discipline, fostering a transparent culture and doing the right thing in every situation. We are equally focused on nurturing talent, respecting the diverse experiences that our team of Risk professionals bring and embracing an inclusive environment.
 
CCB Risk
Chase Consumer & Community Banking (CCB) serves consumers and 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
 
 
The Data Scientist will design, execute, and interpret statistical analyses to drive insights and strategy.  This role requires an individual to leverage creative analytical problem solving skills while using large data sets from a broad range of sources.  In addition to top-tier analytical capabilities, the desired candidate should have exceptional unstructured problem-solving skills coupled with excellent written and oral communication.
 
 
Educational Requirement:
 
PhD with 3+ years, Master’s Degree (MS/MA/MIS/MBA) with 5+ years or Bachelor’s Degree with 8+ years of experience operating as an analytics professional (e.g. data scientist, statistician, and related professions) in a quantitative field: Statistics, Analytics, Data Science, Engineering, Operations Research, Economics, Mathematics, Computer Science, Information Science, and related disciplines.
 
 
Preferred Skills:
 
  • Design, execute, and interpret statistical analyses to drive insights and strategy
  • Experience in the development of statistical and machine learning models (e.g. logistic regression, random forests, gradient boosting, neural networks)
  • Experience in developing credit models and/or analytics to support credit strategy
  • Experience using credit bureau data to develop credit models
  • Leverage creative analytical problem solving skills while using large data sets from a broad range of sources
  • Query enterprise databases using SQL to obtain relevant data for analysis
  • Write computer programming code in R, SAS, Python, and Scala to perform statistical analyses and develop machine learning solutions to business problems
  • Use classical and modern statistical techniques (hypothesis testing, AUC, correlation analysis, clustering analysis) to draw coherent conclusions about the business’s data
  • Effectively reshape data to run efficiently on advanced analytics platforms including Hadoop using Spark
  • Translate classical statistical methods written in SAS, R, and Python into code that can execute using Spark
  • Write summary documentation of analyses performed for both technical and non-technical audiences within the firm
  • Present results to peers and higher levels of management
  • Share advanced machine learning and execution knowledge with team members as new techniques are discovered to solve analytical problems
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