Director, Quantitative Analytics

Hearst•Lawrenceville, GA

About The Position

The Director of Quantitative Analytics will lead the development, enhancement, validation, and governance of Black Book's quantitative models, analytical methodologies, and data-driven valuation solutions across North America. The role will strengthen the connection between statistical science, market data, expert valuation knowledge, and client needs to ensure that Black Book's outputs are accurate, explainable, scalable, and commercially relevant. This leader will work across Data Science, Product, Editorial, Market Insights, and Commercial teams. The position is accountable for establishing disciplined model lifecycle practices, improving analytical depth, developing team capability, and translating complex analysis into clear recommendations for executives, clients, and industry stakeholders. The Black Book Approach Black Book combines proprietary market data, predictive modelling, analyst and editorial expertise, and ongoing market observation to produce trusted vehicle values and forward-looking forecasts. The Director will help ensure that the science and the professional judgement behind each valuation are integrated through a transparent, documented, and repeatable process.

Requirements

10+ years in quantitative modeling, forecasting, or asset valuation, with 4+ years leading technical teams.
Advanced degree in Statistics, Mathematics, Economics, Data Science, Actuarial Science, Engineering, Finance, or a related quantitative discipline.
Significant experience leading quantitative analysis, statistical modelling, forecasting, data science, valuation, risk, or financial analytics in a data-intensive environment.
Demonstrated experience managing and developing analytical or quantitative professionals.
Expert knowledge of predictive modelling, regression, machine learning, forecasting, model validation, performance monitoring, and statistical analysis.
Experience working with large, complex, and longitudinal datasets, including data preparation, feature development, data quality assessment, and reproducible analytical workflows.
Fluency in tools and programming languages such as Python, R, SQL, or equivalent technologies.
Experience establishing model governance, documentation, controls, validation, auditability, and change-management practices.
Strong written and verbal communication skills, with the ability to explain technical concepts, assumptions, limitations, and recommendations to non-technical audiences.
Demonstrated experience with model risk management and governance standards (e.g., SR 11-7 or equivalent) in a regulated or client-audited environment.

Nice To Haves

Experience in automotive, financial services, credit risk, asset valuation, insurance, economics, or another industry involving forecasting and market-sensitive decisions is preferred.

Responsibilities

Oversee residual-value, wholesale, retail, trade, portfolio, and market forecasting methodologies, including segmentation, assumptions, data lineage, and model outputs.
Own the end-to-end residual value modeling lifecycle: data ingestion, feature development, model specification, back-testing, calibration, publication, and post-publication monitoring.
Establish a formal model governance framework: documented methodology, version control, change logs, challenger models, independent validation, and a clear audit trail for every published forecast.
Set the quantitative analytics strategy for North American valuation, forecasting, portfolio analysis, market intelligence, and client-specific analytical solutions.
Establish and maintain model lifecycle standards covering development, independent validation, back-testing, approvals, version control, monitoring, change management, and retirement.
Partner with Editorial teams to ensure model outputs, market observations, constraints, and expert review are reconciled through a controlled and auditable process.
Develop performance monitoring and risk reporting using measures such as MAE, forecast-to-actual variance, stability, responsiveness, and back-testing results.
Translate macroeconomic inputs - interest rates, new vehicle supply and incentives, fuel and energy prices, tariffs, EV adoption curves, off-lease volume - into forward-looking residual assumptions and scenario sets.
Lead scenario analysis and stress testing to assess the effects of economic conditions, interest rates, affordability, supply, demand, incentives, currency, EV adoption, and other market changes.
Ensure Canadian and U.S. models are appropriately governed and aligned while preserving the distinct data, market, and process requirements of each country.
Collaborate with Data Operations to improve data quality, automation, data lineage, reproducibility, and the scalability of analytical processes.
Translate complex methodologies and analytical findings into clear, practical recommendations for senior leadership, Product, Sales, clients, and other non-technical audiences.
Participate in customer discussions to explain methodologies, assumptions, outputs, market insights, and limitations, while gathering feedback to improve solutions.
Support the Model Governance Committee and Residual Values Steering Committee with documentation, validation results, approval recommendations, exception analysis, and performance reporting.
Build, coach, and develop a high-performing team of quantitative analysts, modelers, and analytical specialists, creating succession depth and consistent technical standards.