Director, Collections Strategy & Analytics

Lead AI/ML & BI Capabilities: Direct the team in leveraging modern Business Intelligence tools, advanced analytics, and machine learning techniques to drive actionable insights.

Own Loss Forecasting: Oversee the development, validation, and maintenance of loss forecasting models. Continuously benchmark and refine forecasting methodologies against industry best practices to maximize accuracy.

Experimental Design & Optimization: Champion a rigorous testing culture by designing, executing, and evaluating A/B and multivariate strategy experiments.

Portfolio Analytics: Monitor portfolio performance at a deep segment level to identify emerging risk trends, perform root-cause analysis, and isolate core performance drivers.

KPI Governance: Establish robust Key Performance Indicators (KPIs) and operational dashboards to measure strategic efficiency and financial returns.

Drive End-to-End Strategy: Architect, deploy, and refine integrated customer contact and treatment strategies for both early-stage collections and late-stage recoveries.

Foster Out-of-the-Box Problem Solving: Bring innovative approaches to borrower engagement, digital outreach, and treatment optimization that balance recovery performance with customer empathy.

Executive & Stakeholder Communication: Synthesize complex analytical findings into clear strategic narratives. Regularly present portfolio health, strategy performance, and key metrics to executive leadership and key external partners.

Cross-Functional Synergy: Partner with Credit Risk, Marketing, Product, Engineering, and Operations to seamlessly deploy strategies.

Education: Master’s degree in Statistics, Economics, Mathematics, Engineering, Data Science, Business, or a related quantitative field.

Experience: 10+ years of progressive experience leading advanced analytics and modeling teams in financial services, with direct expertise in consumer lending collections or credit risk management.

Leadership: Proven track record of building high-performing analytical teams, as well as influencing cross-functional stakeholders. Excellent communication and organizational skills.

Technical & Analytical Expertise: Deep expertise in statistical modeling, segmentation, loss forecasting, and modern analytics workflows. Hands-on fluency with tools such as SQL, Python/R, and BI/visualization platforms (e.g., Tableau, PowerBI, Looker).

Agility & Drive: Exceptional ability to synthesize complex data, prioritize high-impact initiatives, and operate with a strong sense of urgency in a fast-paced environment.

Preferred Experience: Hands-on experience building or deploying predictive machine learning models (e.g., propensity to pay, roll-rate models, channel response models).