VP - Enterprise Risk Analytics - BFS

Our client is one of the leading global banking firms which provides industry-focused services for clients across geographies. We are currently looking for a skilled professional to join their Risk team in Bangalore.

Some of the key responsibilities will include:

- Ensuring conceptual soundness and compliance of models (as per the regulatory guidelines), validating models, and coordinating remediation actions as per requirements.

- Ensuring the suitability and soundness of all relevant data, models, and analytics processes for the purpose of enabling underwriting decisions, risk appetite decisions, regulatory estimates, daily business usage, and strategy designing.

- Provide analytical support to the Risk team, and collaborate with other stakeholders in enterprise risk management, Data Strategy, Business and Technology teams.

- Supporting the Risk Team in conducting stress testing and scenario analysis (ICAAP, Climate Risk, etc.), and participating in model implementation and UAT of the same.

- Enhancing assurance of the relevant models, data, and analytics processes and improving the overall risk management processes in the organization by reporting material risk exposures, developing operational risk framework controls, undertaking training initiatives for 1st and 2nd LOD, etc.

To be eligible for this role you will require:

- 10 - 14 years of relevant experience in credit risk modelling or validation, model risk management, and regulatory modelling (as per IFRS9, PRA, UAE guidelines, etc.).

- Bachelor's or Master's degree in Statistics, Finance, Mathematics, or other quantitative fields, along with a thorough understanding of statistical techniques and mathematical concepts.

- Strong background in data analysis and reporting, with demonstrated experience in ensuring reliability and accuracy of financial models.

- Experience in interacting with technical and non-technical audiences, and having strong communication, analytical thinking, and problem-solving skills.

- Proficiency in statistical tools such as Python, SQL / SAS, or R.