Product Manager - Model Governance

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Requirements:

• Bachelor's degree in a relevant field (e.g., Computer Science, Data Science, Finance, Law) or equivalent experience.
• Minimum of 5 years of product management experience, preferably in the financial services or RegTech industry.
• Strong understanding of AI/ML model governance principles and regulatory requirements related to customer screening (AML/KYC, sanctions).
• Experience working with data-intensive products and reporting tools.
• Excellent analytical and problem-solving skills, with the ability to translate complex data into actionable insights.
• Strong communication and1 interpersonal skills, with the ability to collaborate effectively with cross-functional teams and clients.
• Great stakeholder and customer engagement skill
• Strong knowledge of product management lifecycle
• Exposure to an Agile methodology
• Ability to create product development and marketing strategies
• Appreciation of enterprise software design standards and SaaS best practices.

Responsibilities:

• Understand Customer Needs: Liaise with customers and customer-facing staff to fully appreciate their needs
• Define and Drive Product Vision: Develop and maintain a clear product roadmap for model governance reporting, aligned with evolving regulatory landscapes (e.g., AML/KYC, sanctions screening).
• Regulatory Expertise: Maintain a deep understanding of relevant regulations and industry best practices related to AI/ML model governance and customer screening.
• Requirements Gathering and Analysis: Collaborate with compliance, legal, and risk teams to gather detailed requirements for reporting and documentation needed to meet regulatory obligations.
• Report Design and Development: Define and prioritize reporting features, including performance metrics, explainability, audit trails, and bias monitoring, ensuring they meet client and regulatory needs.
• Data Integrity and Validation: Ensure the accuracy and completeness of data used for reporting, working closely with data engineering and data science teams to establish robust data quality controls.
• Client Collaboration: Engage with clients to understand their reporting needs and provide guidance on interpreting and utilizing model governance reports.
• Cross-Functional Collaboration: Work closely with engineering, data science, and UX/UI teams to deliver high-quality, user-friendly reporting solutions.
• Documentation and Training: Create comprehensive documentation and training materials for internal and external stakeholders on model governance reporting.
• Performance Monitoring and Optimization: Continuously monitor the performance of reporting features and identify opportunities for improvement and automation.
• Risk Management: Identify and mitigate potential risks associated with model governance and reporting, ensuring compliance with internal and external policies.
• Staying Current: Keep abreast of the latest advancements in AI/ML model governance and regulatory changes impacting customer screening.