Risk Statistician II

JOB DESCRIPTION

Why GM Financial?

GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, TX We are a global provider of auto finance solutions, with operations in North America, South America, and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses.

At GM Financial, our team members define and shape our culture - an environment that welcomes new ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive.

Our Purpose: We pioneer the innovations that move and connect people to what matters.

This position will be posted until filled.

RESPONSIBILITIES

About the role:

This role focuses on developing and maintaining statistical forecasting models while analyzing complex data to identify trends that impact portfolio performance. The position also involves communicating insights to leadership, validating model accuracy, and creating innovative data visualizations to support business decision-making.

In this role you will:

• Assist in the design, development, and maintenance of sophisticated statistical forecasting models and analytical tools
• Utilize data mining and advanced spreadsheet/technical skills to participate in complex forecasting, modeling, analysis, and reporting related to factors that affect portfolio performance
• Employ best practices of data analysis and model validation to ensure data results are accurate
• Promote innovative ways to visualize and digest complex data
• Effectively summarize and communicate analysis results, expectations, statistical methodology and results to management
• Conduct ad-hoc research projects incorporating project design, data collection and analysis
• May provide direction, training, and guidance to less experienced statisticians and lead projects or assignments as required

• Demonstrated understanding of data mining, data analysis and visualization, quantitative and analytical methods, statistical principles, and sophisticated statistical modeling techniques
• Advanced with Microsoft Excel, PowerPoint, and Word
• Experience with data analysis and spreadsheet modeling and/or reporting
• Experience with coding (SAS or SQL preferred) for data mining and transformation in a data warehouse environment, and for statistical modeling and analysis
• Demonstrated quantitative skills and ability to apply complex statistical principles
• Acute attention to detail
• Effective written and verbal presentation skills
• Capable of managing multiple projects, including ability to coordinate and balance numerous tasks in a time-sensitive environment, under pressure, meeting deadlines
• Ability to identify and understand business issues and map them into quantitative questions
• Understanding of the metrics utilized in monitoring the performance of a consumer lending portfolio is a plus

• Ability to use AI tools (e.g., Microsoft Copilot) to support daily work
• Skills in evaluating AI outputs for accuracy, compliance, and bias
• Experience integrating AI into workflows to improve efficiency or insights
• Familiarity with AI assisted research, summarization, and content generation
• Understanding of responsible AI use, including ethics and data protection

• 2-4 years of experience working with complex Excel workbooks, querying large multi-table datasets, data analysis, data presentation, and statistical analysis and modeling; the qualified candidate will also be able to demonstrate proficiency with the following tools: SAS and/or SQL, Microsoft Excel, PowerPoint, and Word required
• 2-4 years of experience in consumer loan or lease portfolio analysis, modeling and/or forecasting preferred
• High School Diploma or equivalent required
• Bachelor's Degree in Statistics, Applied Mathematics, Econometrics, Operations Research or similar quantitative field; degrees in non-quantitative fields considered with adequate work experience required
• Master's Degree in Statistics, Applied Mathematics, Econometrics, Operations Research or similar quantitative field preferred