Analyst, Credit Risk Mgmt – Modeling & Data Science

Key Skills

Microsoft-Excel, Java Programming, Python Programming, SQL, Machine learning techniques, Data science techniques, R Programming, SAS, Analytical Techniques, Powerpoint, Flax Programming Language

Job Description

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!

Credit Risk organization at T-Mobile is not your typical Financial Services Credit Risk team – it is the team that disrupts and innovates in Credit Risk strategies in support of the UNCARRIER mission!!!

Looking for someone with a quantitative aptitude and a curiosity to unearth the “why” and explain the “so what” hidden in large datasets. The successful candidate will be applying their analytical skills to help the development and monitoring of predictive models used across the T-Mobile customer credit risk life cycle.

You and your team will own and be responsible for developing & maintaining Credit Risk models and attributes to support both consumer and T-Mobile for Business credit risk decision throughout the customer credit lifecycle. You will partner with other functional teams in T-Mobile for deployment, tracking of Credit Risk models and providing read outs to leadership based on model performance insights & analytics.

You must be able to manipulate large amounts of data, extract key insights using data science/statistical/analytical concepts, and then be able to take it up a notch by clearly and concisely communicating actionable recommendations to leadership.

Responsibilities:

• 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

Requirements:

• Bachelor's degree in Analytics, Finance, Economics, Mathematics, Statistics, Engineering, or related fields required; Master’s or PhD degree? a plus
• 2+ 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 required
• 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
• T-Mobile requires all employees in this position to be fully vaccinated for COVID-19, unless precluded from doing so by applicable law. The CDC currently defines “fully vaccinated” as two weeks after the second dose for Pfizer and Moderna, and two weeks after the single dose of Johnson & Johnson. T-Mobile will require proof of vaccination, and will consider requests for exemption from this requirement during the offer phase (1) as a reasonable accommodation for medical reasons or sincerely held religious beliefs where the accommodation would not cause T-Mobile undue hardship or pose a direct threat to the health and safety of others, or (2) for other reasons under applicable law.

Travel:

• Travel Required (Yes/No):No

DOT Regulated:

• DOT Regulated Position (Yes/No):No
• Safety Sensitive Position (Yes/No):No