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Director, Quantitative Analytics
Hearst Health. Lead the development, enhancement, validation, and governance of Black Book’s quantitative models, analytical methodologies, and data-driven valuation solutions across North America .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in quantitative modeling, forecasting, and asset valuation, with a strong focus on model governance and performance monitoring. Capable of leading technical teams and translating complex methodologies into actionable insights for diverse audiences.
Highest-signal resume keywords
Quantitative ModelingForecasting MethodologiesModel GovernancePredictive ModellingStatistical Analysis
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Quantitative AnalysisStatistical ModellingData ScienceMachine LearningPerformance MonitoringModel ValidationData PreparationFeature DevelopmentRisk ReportingScenario Analysis
Soft Skills
Strong Communication SkillsTeam LeadershipCoaching and Development
Tools & Technologies
PythonRSQL
Industry Keywords
Asset ValuationFinancial AnalyticsModel Risk ManagementRegulatory StandardsMarket Intelligence
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Lead the development, enhancement, validation, and governance of Black Book’s quantitative models, analytical methodologies, and data-driven valuation solutions across North America
- Oversee residual-value, wholesale, retail, trade, portfolio, and market forecasting methodologies
- Own the end-to-end residual value modeling lifecycle, including data ingestion, feature development, model specification, back-testing, calibration, publication, and post-publication monitoring
- Establish formal model governance with documented methodology, version control, change logs, challenger models, independent validation, and audit trails
- Set quantitative analytics strategy for North American valuation, forecasting, portfolio analysis, market intelligence, and client-specific solutions
- Establish lifecycle standards for development, validation, back-testing, approvals, monitoring, change management, and retirement
- Partner with Editorial teams to reconcile model outputs, market observations, constraints, and expert review through a controlled process
- Develop performance monitoring and risk reporting using MAE, forecast-to-actual variance, stability, responsiveness, and back-testing results
- Translate macroeconomic inputs into forward-looking residual assumptions and scenario sets
- Lead scenario analysis and stress testing for economic and market changes
- Ensure Canadian and U.S. models are appropriately governed while preserving country-specific requirements
- Collaborate with Data Operations to improve data quality, automation, data lineage, reproducibility, and scalability
- Translate complex methodologies and findings into recommendations for leadership, Product, Sales, clients, and non-technical audiences
- Participate in customer discussions about methodologies, assumptions, outputs, market insights, and limitations
- Support governance committees 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
Requirements
What you’ll need- 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 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
- Experience in automotive, financial services, credit risk, asset valuation, insurance, economics, or another industry involving forecasting and market-sensitive decisions is preferred
- Demonstrated experience with model risk management and governance standards such as SR 11-7 or equivalent in a regulated or client-audited environment
Benefits
Comp & perks- Hybrid work arrangement
- Full-time employment