Data-Driven Credit Risk Analyst

T-Mobile; Overland Park (KS); Presencial; USD 76 000 - 137 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Annual stock grant

Employee stock purchase plan

401(k) matching

Paid time off

12 paid holidays

Resumo da oferta

T-Mobile in Overland Park, KS is seeking an Analyst in the Credit Risk Management team. You will identify credit segmentation opportunities using statistical methods and develop models to enhance credit decisions, collections and fraud strategies.

You will work with data, extract insights, and communicate recommendations, contributing from idea to implementation while collaborating with cross-functional teams to meet deadlines.

Qualificações

• Bachelor's Degree plus 2 years of related work experience or equivalent.
• Proficiency in SAS, SQL, R, Python or other statistical languages.
• Familiar with data visualization tools including Tableau, Power BI, SQL, Excel/VBA, Python (pandas, matplotlib).
• Advanced Excel, Word, PowerPoint skills; strong communication, organization and presentation abilities.
• Strong time management and project management capabilities.

Responsabilidades

• Identify new credit segmentation opportunities using statistical methods.
• Develop predictive financial and analytical models with appropriate methodologies.
• Analyze credit policies and data from multiple sources to meet objectives.
• Collaborate with Credit Risk Management and other teams on deployment and tracking of strategies.
• Communicate actionable insights clearly and concisely to stakeholders.
• Ensure timely delivery of work within project timelines.

Conhecimentos

Analytical thinking

Project management

Presentation skills

Communication skills

Time management

Formação académica

Bachelor's degree + 2 years experience

Ferramentas

SAS

SQL

R

Python

Tableau

Power BI

Excel/VBA

Descrição da oferta de emprego

T-Mobile in Overland Park, KS is seeking an Analyst in the Credit Risk Management team. You will identify credit segmentation opportunities using statistical methods and develop models to enhance credit decisions, collections and fraud strategies.

You will work with data, extract insights, and communicate recommendations, contributing from idea to implementation while collaborating with cross-functional teams to meet deadlines.