Head of Residual Strategy

Based in our suburban Atlanta corporate office and reporting to the President, the Head of Residual Strategy will lead our residual value modeling and predictive valuation capability across the U.S. and Canada.

This is a high-impact leadership role responsible for one of the core economic engines of the business—residual value forecasting and analytics. You will own the strategy, modeling direction, and performance of residual outputs, ensuring they are accurate, stable, explainable, and trusted by the market.

Operating at the intersection of data science, domain expertise, and product, this role translates complex modeling capabilities into customer value, product innovation, and revenue growth while serving as a key external voice for Black Book, representing our residual strategy and market perspective with clients, partners, and industry stakeholders while helping strengthen Black Book’s position as a trusted authority in residual value forecasting and risk analytics.

Residual Strategy & Forecasting

• Own residual value forecasts across new and used vehicles 
• Define and evolve modeling approaches including mileage, trims, volatility, segmentation, and model-year transitions 
• Ensure outputs are accurate, stable, explainable, defensible, and aligned to customer and market needs 
• Support use cases across captives, lenders, ABS/securitization, insurance, and fleet analytics 
• Expand residual capabilities and enable new analytics opportunities 

Modeling Leadership

• Define modeling objectives and success criteria around accuracy, stability, and explainability 
• Partner with Data Science to guide development approaches, feature design, and target definitions 
• Ensure models adhere to established QA, validation, and documentation standards 
• Establish scalable lifecycle processes across development, validation, release, monitoring, and iteration 
• Manage exception handling, volatility controls, and release readiness 
• Ensure strong documentation, terminology consistency, and knowledge transfer 

Customer & Industry Leadership

• Serve as a trusted advisor to captives, lenders, institutional investors, fleet organizations, and insurance carriers 
• Lead resolution of customer escalations and methodology challenges 
• Support strategic sales conversations, portfolio analysis, and risk-related engagements 
• Clearly communicate model behavior, market shifts, and residual trends 

Product & Commercial Collaboration

• Partner with Product to translate residual capabilities into customer-facing solutions 
• Influence roadmap prioritization and monetizable analytics opportunities 
• Support go-to-market efforts through messaging, use case development, and deal support 
• Drive scalable product innovation and revenue-generating opportunities 

Thought Leadership

• Define Black Book’s perspective on residual risk, market volatility, and portfolio performance 
• Lead and contribute to market outlooks, residual value reports, and conference presentations 
• Strengthen Black Book’s position as a trusted authority in residual analytics and forecasting 

Cross-Functional Leadership

• Lead a dedicated Residual Data Science team and Residual Analyst function 
• Partner across Data Science, Product, Data & Market Intelligence, BI, Sales, and Marketing 
• Drive alignment across teams to ensure clear ownership, efficient execution, and consistent outputs 

What Success Looks Like

• Residual outputs meet defined accuracy, stability, and explainability targets 
• Customers trust Black Book to support portfolio risk and strategic planning decisions 
• Residual analytics drive scalable product innovation and new revenue opportunities 
• Black Book strengthens its reputation as a leader in residual value forecasting 

Experience

• 15+ years in auto finance, residual modeling, forecasting, and/or related analytics domains
• Bachelors degree in Data Science or related field such as Mathematics, Statistics or computer science. Masters or PhD strongly preferred 
• Deep understanding of leasing economics, residual risk, and ABS/securitization markets 
• Experience with statistical modeling, machine learning approaches, and AI adoption 
• Experience working directly with captives, lenders, and institutional investors 

Capabilities

• Ability to bridge technical modeling and business strategy 
• Strong understanding of model behavior, limitations, and explainability 
• Ability to communicate complex outputs clearly to technical and business audiences 
• High credibility in customer-facing environments 

Nice to Have

• Experience building or scaling data products 
• Exposure to API-based delivery and analytics platforms 
• Experience working with production AI/ML systems 

What This Role Is Not

• Not a pure data science execution role 
• Not a traditional product manager 
• Not an internal-only analytics role

Company Background: