Credit Acceptance
Manager, Predictive Modeling and Machine Learning
USA - Remote · Senior
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odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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About the role
- 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.
Requirements
- Bachelor's degree in a quantitative discipline.
- 5+ years of experience in predictive modeling and analytics.
- 5+ years of experience applying ML in production environments.
- Advanced proficiency in Python, SQL, and statistical tools (e.g., R, SAS).
- Attendance as required by department
Nice to have
- Ph.D or Master's in quantitative discipline.
- Experience with model governance, explainability, and responsible AI.
- Experience in consumer lending.
Compensation
- $158,000 - $170,000 base salary + annual bonus plan.
Benefits
- Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
Company info
- We're proud of our culture and are happy to share why - let's talk!
- As part of our Culture of Compliance, we are proud to be an Equal Opportunity Employer and value our culturally diverse workforce.
Visa & Work Authorization
- This position is not currently open to individuals who require sponsorship now or in the future to work legally for Credit Acceptance, such as H-1b / H-4 or F-1 OPT visa holders.
This listing is sourced directly from Credit Acceptance's careers page and normalized into a canonical job model.