DWS Enterprise AI Product Manager

Key Responsibilities

Strategy & Leadership

• Define 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 Development

• Credit & 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 & Technology

• Data 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 & Culture

• Talent 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-making

Qualifications

Education & Experience

• Bachelor's degreein Computer Science, Data Science, Engineering, Finance, or related fieldMaster's or PhDpreferred.
• 10+ yearsof experience in data science, AI/ML, or analytics leadership roles, with5+ yearsspecifically in financial services or leasing/finance industries.
• Proven track record of deploying AI/ML solutions in production at scale within regulated financial environments.

Technical Skills

• Deep expertise in machine learning (supervised/unsupervised learning, deep learning, NLP, time series forecasting) and generative AI applications.
• Proficiency in Python/R, SQL, and cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
• Experience with MLOps practices (model CI/CD, monitoring, versioning) and ML frameworks.
• Understanding of leasing economics, credit risk modeling, and financial analytics-including NPV, NER, and cash flow modeling.
• Knowledge of lease accounting standards, regulatory compliance, and data privacy (GDPR, CCPA).

Business & Leadership Skills

• Strong commercial acumen with ability to translate complex technical concepts into business value propositions for executive stakeholders.
• Experience building and leading high-performing data science teams in fast-paced environments.
• Exceptional communication and influencing skills ability to drive change across organizational silos.
• Creative problem-solver who can think beyond conventional financial services approaches.

Key Success Metrics

• Business 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.