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As an Analyst within the Credit Risk Management team, you will wear multiple hats. You will use your strong blend of analytical skills, project management, presentation skills, and ability to develop strong and effective working relationships. You will work independently, and/or as part of a team of analysts, to identify new credit segmentation opportunities using statistical methods leading to enhancements to our credit decision process and policies, collections strategies, and fraud strategies. You will partner within members of the Credit Risk Management team and other functional teams in T-Mobile for the deployment, analysis and tracking of new credit strategies to manage risk.
Job Responsibility:
Utilize statistical segmentation techniques to identify new opportunities
Extract, load and transform data from multiple sources necessary for statistical, reporting and ad-hoc analysis
Perform qualitative and quantitative analysis of credit polices to meet given objectives
Develop predictive financial and analytical models using the appropriate statistical methodologies, including trend and regression analysis
Participate and perform the analysis of new data and statistical products by external vendors
Work with credit bureau and alternative credit data
Deliver work output with full awareness and adherence to project timelines or agreed upon deadlines
Requirements:
Bachelor's Degree Finance, Economics, Mathematics, Industrial Engineering, Statistics or related degree
BA/BS in Finance, Economics, Mathematics, Industrial Engineering, Statistics or related degree
Intermediate or higher proficiency in SAS or other statistical/analytical programming languages
Superior computer skills in Excel, Word, PowerPoint required
Superior communication (spoken and written), organization and presentation skills
Superior time management skills and awareness of project management methods
Nice to have:
3+ years of quantitative analytic modeling experience, credit risk management or comparable process management experience
Graduate degree in a quantitative discipline a plus
Basic proficiency in SQL, C++, Python, R or other statistical software packages a plus
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