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Tier-1 employer, metro location, and mid-level experience make applicant competition high despite niche skills.
Role requires finance-specific model validation and regulatory experience, limiting cross-industry transferability.
Explicit 5-7 years requirement plus mandatory quant, ML, programming, and regulatory expertise raises shortlisting strictness.
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Lead and independently conduct validation exercises for investment, financial, and AI/ML models including advanced language models like GPT.
Manage and maintain model inventory and risk assessment processes while ensuring compliance with FIL's Model Risk Policy and evolving regulatory requirements.
Engage with stakeholders across business and model owners to identify issues, communicate findings, and recommend remediation actions in model risk management.
5-7 years of experience in quantitative modelling, model validation, or risk roles within financial services (preferably investment banking or asset management).
Advanced degree (Ph.D. or Master's) in Mathematics, Statistics, Computer Science, or related quantitative discipline preferred.
Proficiency in programming languages related to AI/ML such as Python, TensorFlow, or PyTorch; strong MS Excel and VBA skills; knowledge of model library programming in C++, Java, Python, or others.
Solid understanding of various model types including investment, pricing, risk, capital, and AI/ML models; familiarity with fine-tuning/deploying large language models is advantageous.
Experienced in end-to-end model validation, capable of translating complex quantitative analysis into actionable recommendations for technical and non-technical stakeholders.
Operates effectively in a regulatory-driven environment with strong focus on compliance to model risk policies and adapting to emerging AI/ML technologies.
Demonstrates hands-on management of model validation processes and collaborates well with internal stakeholders to support governance and knowledge sharing in model risk.