Data Analyst I - Model Management

GM Financial•Fort Worth, TX

4d•Hybrid

About The Position

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry. Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact. This position will be posted until filled.

Requirements

Knowledge of the data science lifecycle and model risk management.
Familiarity with data visualization best practices.
Familiarity with model governance and monitoring standards.
Understanding of statistical and machine learning techniques.
Knowledge of on‑premise and cloud-based data platforms.
Hands‑on experience with Power BI dashboard development.
Strong SQL skills in a data warehouse environment.
Ability to read and understand analytical code written by others in SAS or Python.
Strong communication skills for technical and non‑technical audiences.
Ability to work independently with minimal supervision.
Experience analyzing large datasets.
Prior analytics or reporting experience.
Bachelor’s degree in Economics, Mathematics, Business Analytics, Computer Science, Statistics or other quantitative field required.

Nice To Haves

Cloud (Azure, AWS, Databricks) and Git experience preferred.
Master’s degree in Economics, Mathematics, Business Analytics, Computer Science, Statistics or other quantitative field preferred.

Responsibilities

Analyze large and complex datasets to identify trends, risks, and opportunities.
Design, build, and maintain Power BI dashboards and reports.
Partner with stakeholders to translate business needs into clear metrics and visualizations.
Ensure data accuracy, consistency, and clarity across reports and dashboards.
Document metric definitions, assumptions, and reporting logic.
Act as a liaison between model governance, data science, and oversight functions.
Support basic model monitoring and governance activities.