Analyst, Credit Risk Management - Modeling and Data Science

at  TMOBILE

BE UNSTOPPABLE WITH US!

T-Mobile is synonymous with innovation–and you could be part of the team that disrupted an entire industry! We reinvented customer service, brought real 5G to the nation, and now we’re shaping the future of technology in wireless and beyond. Our work is as exciting as it is rewarding, so consider the career opportunity below as your invitation to grow with us, make big things happen with us, above all, #BEYOU with us. Together, we won’t stop!
Join our dynamic Credit Risk organization at T-Mobile. We are not just a team; we are the driving force behind disrupting and innovating in Credit Risk strategies to champion the UNCARRIER mission!
As a key player in our team, you’ll bring your quantitative aptitude and curiosity to uncover the “why” and articulate the “so what” embedded in extensive datasets. This role will contribute to the development and monitoring of predictive models across the T-Mobile customer credit risk life cycle.
Within this environment, you will take ownership and be responsible for developing and maintaining Credit Risk models and attributes. These models and attributes play a crucial role in supporting credit risk decisions for both consumers and T-Mobile for Business throughout the customer credit lifecycle. Your collaborative spirit will shine as you partner with other teams in T-Mobile, to ensure seamless deployment, tracking of Credit Risk models, and delivering insightful leadership communications based on model performance insights and analytics.

Success in this role hinges on your ability to handle vast amounts of data, draw out key insights using data science, statistical, and analytical concepts, and then elevate your findings by communicating clear and concise actionable recommendations to leadership. Join us in reshaping the landscape of Credit Risk – where “We” make a difference!

• Develop a deep understanding of T-Mobile systems, data and business processes across the customer credit lifecycle
• Develop, maintain, and monitor credit attributes from both external credit bureau data as well as internal customer behavior data
• Develop, maintain, and monitor Credit Risk models emphasizing both prediction accuracy, and the impact to losses and profitability
• Solve complex customer behavior problems using data science/machine learning/NLP approaches
• Participate and perform the analysis of new data and statistical products by external vendors
• Deliver work output with full awareness and adherence to project timelines or agreed upon deadlines

REQUIREMENTS:

• Bachelor’s degree in Analytics, Finance, Economics, Mathematics, Statistics, Engineering, or related fields required; Master’s or PhD degree a plus
• 3+ years of quantitative analytics experience in academic or business setting required; working experience in Financial Services industry a plus
• 1+ years of predictive modeling development experience required
• Basic understanding in credit bureau report data and credit bureau attributes desired
• Solid hands-on experience in SAS, Python, R or other analytical/model building programming languages required
• Intermediate or higher proficiency in SQL required
• Hands-on experience with Excel, Word, PowerPoint required
• High communication, organization, presentation, and data visualization skills required
• Superior time management skills required
• Wireless / Telecom experience a plus
• At least 18 years of age
• Legally authorized to work in the United States

• Develop a deep understanding of T-Mobile systems, data and business processes across the customer credit lifecycle
• Develop, maintain, and monitor credit attributes from both external credit bureau data as well as internal customer behavior data
• Develop, maintain, and monitor Credit Risk models emphasizing both prediction accuracy, and the impact to losses and profitability
• Solve complex customer behavior problems using data science/machine learning/NLP approaches
• Participate and perform the analysis of new data and statistical products by external vendors
• Deliver work output with full awareness and adherence to project timelines or agreed upon deadline