Credit Risk Management Analyst – Strategy

• Utilize statistical segmentation techniques to identify new opportunities
• Extract, load and transform data from multiple sources for statistical, reporting and ad-hoc analysis
• Perform qualitative and quantitative analysis of credit policies to meet objectives
• Develop predictive financial and analytical models using statistical methodologies, including trend and regression analysis
• Analyze new data and statistical products from external vendors
• Work with credit bureau and alternative credit data
• Deliver work output within project timelines and agreed-upon deadlines
• Identify credit segmentation opportunities to enhance credit decision processes, policies, collections strategies and fraud strategies
• Partner with Credit Risk Management and other T-Mobile functional teams on deployment, analysis and tracking of credit strategies
• Manipulate large datasets, extract insights and communicate actionable recommendations
• Participate in projects from hypothesis generation through development and implementation
• Contribute creative, credit-focused ideas and demonstrate problem-solving and ownership

• 2-4 years experience in a qualitative analytical function
• Bachelor's Degree 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, Power Point
• Superior communication (spoken and written), organization and presentation skills
• Superior time management skills and awareness of project management methods
• At least 18 years of age
• Legally authorized to work in the United States
• Graduate degree in a quantitative discipline a plus
• Basic proficiency in SQL, Python, R or other statistical software packages
• Prior consumer risk management experience a plus
• Wireless / Telecom experience a plus

Demonstrates expertise in statistical analysis and data manipulation to drive credit decision processes and strategies. Proficient in developing predictive models and collaborating with cross-functional teams to enhance credit risk management.

• Statistical Segmentation Techniques
• Predictive Financial Modeling
• SAS Proficiency
• Credit Risk Management
• Data Analysis

• Statistical Analysis
• Trend Analysis
• Regression Analysis
• Data Transformation
• Qualitative Analysis
• Quantitative Analysis
• SQL Proficiency
• Python Proficiency
• R Proficiency
• Excel Proficiency

• Communication Skills
• Organizational Skills
• Presentation Skills
• Time Management Skills
• Problem-Solving Skills

• Credit Policies
• Consumer Risk Management
• Wireless
• Telecom
• Credit Segmentation

• SAS
• Excel
• Word
• Power Point
• Statistical Software Packages