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T-Mobile Sr Credit Risk Manager - Modeling & Data Science in Overland Park, Kansas

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!!!

We are looking for an individual with a superior blend of Business, Data Science background, Credit Risk modeling skills, advanced presentation skills, solid Credit Risk experience and People skills. You will work closely with the Credit Risk Management leadership team, Consumer and T-Mobile for Business [TfB] Credit Strategy and Collections Strategy teams to develop/maintain Credit Risk models and solve complex customer behavior and advanced analytical problems.

You and your team will own and be responsible for developing & maintaining Credit Risk models and attributes to support both consumer and TfB 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 from data 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. You will be expected to represent Credit Risk Management team in the role of a credit data, credit risk modeling and data science/machine learning expert.

This is an individual contributor position and does not manage people.

What you’ll do in your role.

  • Develop a deep understanding of industry, corporate, and customer drivers to leverage in optimization of all aspects of our decision strategy and business processes across the 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

  • Lead and mentor junior team members to grow on technical capabilities and industry knowledge

The experience you’ll bring.

  • Bachelor’s in Finance, Economics, Mathematics. Statistics or related degree required. Master’s or PhD in Analytics, Finance, Economics, Mathematics, Industrial Engineering, Statistics or related degree​ preferred

  • 7+ years of Quantitative Analytics experience in the Financial Services industry

  • 4+ years of Credit Risk model development experience

  • Expert in credit bureau report data and derived credit bureau attributes

  • Solid hands-on experience in SAS or other statistical/analytical model building/programming languages

  • High proficiency in Python, R or other machine learning software required

  • Hands-on experience with credit decision system and implementation

  • Superior skills in Excel, Word, PowerPoint required

  • Superior communication, organization, presentation and data visualization skills

  • Superior time management skills and awareness of project management methods

  • Wireless / Telecom experience a plus

  • At least 18 years of age

  • Legally authorized to work in the United States

  • Bachelor’s Degree

  • T-Mobile requires all employees in this position to be fully vaccinated for COVID-19 prior to starting work. The CDC 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 consider requests for exemption from this requirement during the offer phase 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.

Position details

Req ID: 179821BR

Department: Finance

Travel Required: No