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Regular or Temporary:
Language Fluency: English (Required)
1st shift (United States of America)
Perform sophisticated analytics (statistical and predictive analytics, machine learning modeling, etc) to provide actionable insights that improve business outcomes and minimize risk. Provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies throughout the entire lending product credit life cycle.
Independently performs various analytic techniques (encompassing data mining, inferential statistical analysis, and predictive analytics, for example) to support the entire credit life cycle of the lending product.
Leveraging strong foundation of business and industry knowledge, identifies actionable insights from various (or multiple) sources of data that measurably improve business outcomes or reduce business risk.
Collects and prepares data for analysis, performs exploratory to advanced predictive and/or modeling analytics, and identifies data relationships (patterns and trends).
Provides consultation to business leaders and other stakeholders on how to leverage analytic insights to build actionable strategies. Crafting effective presentations and delivering results to multiple levels of management.
Provide analytic recommendations for credit policy development, credit initiatives (underwriting, new products, new populations, new channels, portfolio strategies, line management, pricing, etc.), and monitor initiatives to ensure that performance is within established risk appetite.
Utilize internal and external data to drive decision-making and identify opportunities. Monitor trends in the industry. Seeks out new sources of information for benchmarking and strategy development.
Focus on getting the most out of data and exploring new areas of analysis.
The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1. Bachelor's degree in Business, Computer Science, Data Science, Engineering, Economics, Statistics or related field, equivalent education, or related training
2. 4 years large bank experience in credit risk management, risk strategy, and/or risk analytics across diverse high volume lending businesses (e.g., small business, credit card, automotive, fintech)
3. Experience with data mining, data programming (e.g., SAS, SQL, R, Python), and data visualization (Tableau, Excel, Powerpoint) working with large, often disparate, corporate datasets
4. Outstanding communication and presentation skills; ability to distill and translate complex data and strategies to diverse stakeholders in clear, crisp terms
1. Master's degree, equivalent additional education or related training (e.g., graduate school of banking, CFA, SAS certifications)
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