Manager - Model Risk

About you:

Key Responsibilities:

- To develop a strong understanding of FIL's Model Risk Policy and procedures and support education efforts with internal and external stakeholders.

- Independently lead model validation exercises, applying rigorous analysis to assess model accuracy, robustness, and compliance with FIL's Model Risk Policy or other relevant regulations.

- Identify potential issues and discrepancies and engage in open discussions with stakeholders to reach consensus on appropriate remediation actions.

- Translate complex technical findings into clear and actionable recommendations for both technical and non-technical stakeholders.

- Act as a subject matter expert, providing guidance and insights to model owners and developers, ensuring compliance with FIL's Policy and procedures.

- Stay abreast of current trends in AI and ML model usage and controls, especially within asset management, and apply this knowledge to model validation processes.

- Stay informed about evolving regulatory requirements related to AI and ML model usage in various geographies and ensure compliance in model validation.

- Identify and address challenges associated with large and complex AI and ML models and develop innovative solutions for effective model risk control.

- Implement validation frameworks for emerging technologies like Retrieval Augmented Generation (RAG) and agent-based systems.

- Foster a collaborative environment within the Model Risk Team, promoting knowledge sharing and teamwork.

- Establish and maintain highly effective working relationships with all key internal stakeholders.

- Support model governance activities.

- Contribute to the quality of the inventory data, including reporting data, materiality assessments, etc.

- Support the development of modelling standards.

- To provide SME advisory and assurance that models meet policy and regulatory requirements.

Experience and Qualifications Required:

- Solid understanding of all model types (investment, pricing, risk & capital) including AI and ML models - natural language processing (NLP), deep learning, and advanced language models.

- Good understanding on fine-tuning and deploying large language models (e.g., GPT, LLaMA) for domain-specific tasks such as test case generation, summarisation, and anomaly detection would be advantageous.

- 5-7 years of experience in quantitative modelling and model validation and risk related roles within the financial industry - investment banking or asset management is preferable.

- Advanced degree (Ph.D. or Master's) in a quantitative field such as Mathematics, Statistics, Computer Science, or a related discipline preferred.

- Proficiency in programming languages commonly used in AI and ML, such as Python, TensorFlow, or PyTorch.

- Strong MS Excel & expertise with VBA, model library programming (C++, Java, Python &/or other).

- Ability to demonstrate project delivery across model validation, model documentation will be highly advantageous.

- Hands-on mentality.

- Excellent analytical, problem-solving and written skills.

- Strong people management skills and experience.

- Excellent at providing innovative ideas and comfortable working under pressure.

- Dedicated to maintaining high quality standards and client satisfaction.

- Hands on experience building, enhancing and running model validation processes is essential.

- CFA/FRM/CQF and/or strong expertise in AI and ML models, would be advantageous.