Jobs at Credit Acceptance Corporation

Leads marketing insights and planning, unifying consumer, competitive, market, and dealer data into actionable strategies. Oversees segmentation, KPI dashboards, long-range and annual planning, budgeting, business cases, CDP requirements, and AI-enabled automation. Serves as chief of staff and advisor to the CMO, presents to executives and the board, and builds and mentors a multi-layered insights and analytics team.

Leads consumer research and segmentation, selecting qualitative, quantitative, or secondary methods to generate efficient insights. Translates customer needs and behaviors into marketing strategy, CDP requirements, and growth and retention initiatives. Presents actionable recommendations to executive audiences, manages research vendors, panels, and budgets, applies AI to accelerate insight generation, and builds a high-performing consumer insights team.

Leads the customer loyalty and retention function, owning lifecycle strategy, retention, gross dollar retention, and customer satisfaction outcomes. Builds loyalty programs, maps customer journeys, uses qualitative and quantitative insights to prioritize initiatives, manages budgets and external partners, and presents recommendations to executives. The role also builds and develops a high-performing team, partners cross-functionally, and drives large-scale program launches and continuous improvement.

Lead enterprise planning, forecasting, and performance management. Translate strategy into driver-based models, deliver accurate forecasts, establish KPIs and governance, enable modern reporting and analytics, track initiative value, and partner with executives to drive data-driven decisions and accountability across the organization.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

Leads commercialization and product marketing for pricing, profitability, and transaction-enablement products. Develops positioning, messaging, go-to-market strategies, launch plans, customer communications, and sales enablement. Conducts customer and competitive research, analyzes adoption and performance data, defines success metrics, and recommends optimization strategies. Partners with Product, Pricing, Sales, Analytics, and Customer Success teams to increase customer engagement, product adoption, and revenue growth.

The Senior Product Manager will enhance customer experiences, drive product strategies, manage lifecycle stages, and collaborate with teams to increase satisfaction and revenue growth.

The Senior Product Manager advocates for customers, enhances product experiences, analyzes data for improvements, and leads product development strategies while collaborating with technical teams.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Owns the technical strategy, roadmap, requirements, and delivery of scalable data solutions. Partners with business, product, design, and engineering teams to prioritize high-impact features, assess vendors, guide architecture, incorporate AI strategies, and resolve technical issues. The role requires strong BI and data warehouse expertise, technical leadership, stakeholder communication, agile product development, customer delivery support, and mentoring responsibilities.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead and develop Directors of Sales to execute regional strategy at district and market levels, drive sales and P&L results, strengthen dealer relationships, enforce financial discipline, coach leaders, manage performance, and partner cross-functionally. Requires heavy travel and ownership of market execution and organizational health.

Lead FP&A strategy, budgeting, forecasting, financial modeling, management reporting, and analytics. Advise CFO and executives on capital allocation, M&A evaluation, and performance improvement while building a high-performing FP&A team and advancing finance technology and AI-enabled decision support.

The Senior Data Modeler designs and maintains data models, supports AI-driven practices, and collaborates on data lifecycle processes. Responsibilities include translating business needs into data structures, ensuring data governance, and supporting analytics and AI use cases.

Perform analysis of loan servicing strategies, monitor champion/challenger tests, develop complex data extraction and manipulation code, produce performance reports, and translate business goals into technical tasks to optimize high-volume decisions.

Senior individual contributor owning AI/ML products end-to-end: discover opportunities, define roadmaps, make build-vs-buy decisions, lead vendor evaluations and cross-functional delivery, monitor KPIs and operational signals, and drive continuous improvement and adoption.

Design, develop, implement, and monitor statistical and machine learning models to support credit risk, collections, servicing, marketing, and forecasting. Build data pipelines, translate business needs into analytical solutions, support model governance, testing, deployment, and ongoing performance monitoring, and communicate results to stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.