AI Product Manager

Key Responsibilities

Strategy & LeadershipDefine the AI Vision: Develop and execute a comprehensive AI roadmap aligned with the leasing business strategy, prioritizing high-impact use cases across the lease lifecycle (origination, underwriting, servicing, collections, asset management) .Establish the CoE Framework: Build the governance, operating model, and best practices for AI adoption, including model development standards, ethical AI guidelines, data privacy protocols, and performance measurement frameworks.Stakeholder Engagement: Partner with Commercial, Credit, Risk, Operations, and DT leaders to identify pain points and opportunities where AI can drive efficiency, revenue growth, and risk reduction.Solution DevelopmentCredit & Risk Intelligence: Lead development of AI/ML models for credit scoring, lease default prediction, and portfolio risk analytics—leveraging both traditional financial data and alternative data sources .Process Automation: Deploy NLP and generative AI solutions to automate lease document review, contract analysis, and compliance checking; streamline RFP responses and lease negotiation workflows .Commercial Optimization: Build predictive analytics for lease pricing optimization (NPV/NER modeling), tenant retention scoring, and market intelligence—analyzing comparable properties, pricing trends, and competitive positioning .Servicing & Collections: Implement AI-driven customer service (chatbots, intelligent routing) and collections optimization models to improve recovery rates and customer experience.Data & TechnologyData Strategy: Define data requirements, ensure data quality, and establish data pipelines to feed AI models—integrating internal systems (leasing management, CRM, ERP) with external data sources.Technology Selection: Evaluate and select AI/ML platforms, MLOps infrastructure, and vendor solutions; oversee the build vs. buy decisions for AI capabilities.Model Governance: Establish rigorous validation, monitoring, and retraining protocols to ensure model accuracy, fairness, and regulatory compliance.Team Building & CultureTalent Acquisition: Recruit, mentor, and lead a cross-functional team of data scientists, ML engineers, data analysts, and AI product managers.Change Management: Champion AI adoption across the organization, delivering training programs to upskill commercial and operational teams in leveraging AI tools .Innovation Culture: Foster a culture of experimentation and continuous learning, encouraging rapid prototyping and data-driven decision-makingQualificationsEducation & ExperienceBachelor's degree in Computer Science, Data Science, Engineering, Finance, or related field; Master's or PhD preferred.10+ years of experience in data science, AI/ML, or analytics leadership roles, with 5+ years specifically in financial services or leasing/finance industries.Proven track record of deploying AI/ML solutions in production at scale within regulated financial environments.Key Success MetricsBusiness Impact: Measurable improvements in lease origination velocity, credit loss reduction, portfolio yield optimization, and operational cost savings.Model Performance: Accuracy, fairness, and stability of AI models; adherence to governance and compliance standards.Adoption Rates: User adoption of AI tools across commercial, credit, and operations teams.Innovation Pipeline: Number of new AI use cases identified and piloted annually.