Vice President - Modeling & Quant Analytics (MRG)

Skills And Competencies

• A deep understanding of quantitative finance, modeling, and model validation, coupled with practical experience, is required, along with a good understanding of financial products and markets.
• A deep understanding of AI model risk management, including risks and controls specific to Generative AI and Agentic AI, and their implications for model validation and governance.
• Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency.
• Strong experience using AI tools to lead innovation initiatives, demonstrated leadership in managing AI‑related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization.
• The ability to articulate complex ideas fluently and clearly.
• Proficiency in programming languages such as R, Python, MATLAB, and SQL, with the ability to work within an established code base. Knowledge of C++ programming is preferred.
• Previous experience in model validation and development, particularly in areas such as credit risk, counterparty credit risk, or market risk, is necessary.
• 10+ years of experience in model development, validation, and risk analytics is necessary.
• Build and nurture relationships: Inspire and encourage team members to build strong, collaborative relationships.
• Provide timely feedback and coaching: Offer constructive feedback and coaching to help team members optimize their potential.
• Role‑model growth mindset: Foster innovation and advance the team’s evolution by demonstrating a growth mindset.
• Encourage agile thinking and adaptability within the team.
• Translate strategy into actionable goals: Ensure the team focuses on critical priorities by setting clear, actionable goals.
• Establish a compelling vision: Provide clear direction and a compelling vision for the team to follow.
• Create a culture of trust: Honor commitments and treat everyone with respect to build a culture of trust.
• Highly organized, efficient, and detail‑oriented, demonstrating a high level of accuracy in their work.
• Possesses excellent organizational skills, capable of multitasking and prioritizing work effectively within a team environment.
• The ability to work under tight deadlines, manage time efficiently, and maintain focus is essential.
• Capable of working both independently and collaboratively, with strong English written and spoken communication skills.

Education

• A strong academic background in a quantitative field such as mathematics, artificial intelligence, physics, economics or engineering, or a program focused on a quantitative discipline is essential, with a preference for candidates holding a post‑graduate degree.

Responsibilities

• Execute and lead independent validation reviews of credit rating models, scorecards, including hands‑on assessment of inputs, assumptions, conceptual soundness, performance, and limitations.
• Lead AI model risk management activities end‑to‑end, including identification, assessment, and mitigation of risks specific to Generative AI and Agentic AI.
• Design and deliver complex validation analyses, including model replication, challenger development, sensitivity testing, benchmarking, and ad‑hoc quantitative investigations.
• Own validation outputs end‑to‑end, from test plan design through clear, concise validation reporting and effective challenge of model developers.
• Develop AI model risk management capability within MRG, mentoring validators, developing GenAI and Agentic AI expertise, and actively contributing as a subject‑matter expert across analytical and methodology teams.
• Drive rigorous execution standards and culture, promoting effective challenge, continuous improvement, and practical application of model risk frameworks and policies.

About The Team

The MRG Quantitative Review team independently reviews and validates quantitative models and scorecards that support credit ratings. It notably assesses whether these tools are conceptually sound, appropriately calibrated, and fit for purpose, and that they operate in line with approved methodologies and governance standards.