Credit Acceptance
Manager, Predictive Modeling and Machine Learning
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Job Description
Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually and together as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we’ve grown into a leading provider of used and new car financing across the country.
Our Engineering and Analytics Team Members utilize the latest technology to develop, monitor, and maintain complex practices that help optimize our success. Our Team Members value being challenged, are encouraged to express their ideas, and have the flexibility to enjoy work life balance. We build intrinsic value by partnering with all functions of our business to support their success and make strategic business decisions. We focus on professional development and continuous improvement while enjoying a casual work environment and Great Place to Work culture!
The Manager of Predictive Modeling & Machine Learning will lead the development and deployment of statistical and machine learning models that drive strategic decision-making across credit risk, collections, and business operations. This role combines expertise in traditional predictive modeling with modern ML techniques and MLOps practices. The ideal candidate will be an expert-level hands-on modeler with strong technical skills and business acumen. Outcomes and Activities:
- This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required. However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member.
- Lead the design and deployment of predictive models for credit risk, collections, and operations.
- Integrate advanced ML techniques (e.g., ensemble models, deep learning, NLP).
- Mentor and support junior and peer-level data scientists.
- P...
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