AI Product Manager

Lenovo; Petaling Jaya; On-site; MYR 200,000 - 420,000; Full time

Job summary

Lenovo seeks a seasoned AI/ML strategy and analytics leader in Malaysia to define the AI vision and build a Center of Excellence supporting leasing business processes. You will drive governance, data privacy, and performance metrics while partnering with Commercial, Credit, Risk, and Operations leaders to maximize efficiency and revenue.

The role focuses on credit risk intelligence, NLP-based automation, predictive pricing, and portfolio analytics, with a strong emphasis on governance and

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Finance, or related field; Master’s or PhD preferred.
• 10+ years in data science, AI/ML, or analytics leadership roles; 5+ years in financial services or leasing/finance.
• Proven track record deploying AI/ML solutions in production at scale within regulated financial environments.

Responsibilities

• Define the AI Vision: Develop and execute a comprehensive AI roadmap aligned with the leasing business strategy and lifecycle.
• Establish the CoE Framework: Governance, operating model, best practices, data privacy protocols, and performance metrics.
• Stakeholder Engagement: Partner with Commercial, Credit, Risk, Operations, and DT leaders to identify AI opportunities.

Skills

AI strategy & leadership

Stakeholder engagement

Model governance

ML model development

Data governance

Education

Bachelor's degree in CS/Data Science/Engineering/Finance

Master's or PhD preferred

Tools

Python

SQL

TensorFlow / PyTorch

Job description

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 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 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.