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Medium due to metro location and mid-level band, balanced by niche model-risk specialization and industry preference.
High because model-risk, regulatory and asset-management validation experience is industry-specific and not easily transferable.
High because of explicit 5–7 years requirement, advanced quantitative degree preference, and mandatory ML plus finance validation skills.
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Lead and conduct rigorous validations of investment, financial, AI/ML, and advanced language models to assess accuracy, robustness, and compliance with model risk policies.
Manage the model inventory and risk assessment process while engaging and collaborating effectively with model owners, developers, and other key stakeholders.
Implement and innovate validation frameworks for emerging AI technologies such as Retrieval Augmented Generation and agent-based systems, ensuring adherence to regulatory requirements.
5-7 years of experience in quantitative modelling, model validation, or risk roles within the financial industry (investment banking or asset management preferred).
Advanced degree (Master's or Ph.D.) in Mathematics, Statistics, Computer Science, or related quantitative field.
Proficiency in AI/ML programming languages and frameworks such as Python, TensorFlow, or PyTorch; strong skills in MS Excel and VBA, with additional programming in C++, Java, or Python.
Solid understanding of investment, pricing, risk, capital, and AI/ML models including natural language processing and advanced language models.
Experienced in hands-on model validation processes with a track record of delivering projects involving complex quantitative and AI/ML models in a regulated financial environment.
Strong subject matter expert able to translate complex technical model validation findings into actionable advice for both technical and non-technical stakeholders.
Capable of managing stakeholder relationships effectively within cross-functional teams and driving collaboration to maintain high standards of model risk governance.