Machine Learning Engineer, Senior Manager

At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation.

Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact.

• Lead the vision and the strategic execution with a strong focus on continuous and long-term value creation across all participants of our flywheel
• Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans.
• Drive execution and delivery of ML/AI solutions by managing priorities, deadlines, and deliverables, leveraging your technical expertise.
• Design and deliver scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices, and nurture the culture of creating high-quality, well-tested systems to address critical product and business needs.
  • Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency.
  • Ensure the security, scalability, and architectural integrity of feature designs through reviews across teams.
  • Deliver hands-on solutions while mentoring other data professionals (including MLEs) within the organization
• Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization.
• Guide a team of MLEs across different areas:
  • Mentoring:
    Mentor team members on design principles, coding standards, and the adoption of AI productivity tools.
  • Recommendations – Personalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi-armed bandits
  • Growth:
    Foster long-term growth through data-driven causality and incrementality
  • Gen-AI:
    Power existing applications with Gen AI models and engineering to improve downstream experience and decisions
  • Lifecycle
    - Using ML models (such as XGBoost & Causal Meta-Learner-based model, etc), proactively guide business teams across different areas
  • Engineering
    - With engineering partners, build ML and Gen-AI platform and inference pipelines for different types of models

• Customer Empathy:
  Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer’s shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.
• Engineering Excellence:
  Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.
• One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective…