Description of role and key responsibilities The role is within the ALM team and focuses on supporting the day-to-day activities of the team involving daily monitoring, analysis, development and validation of risk models in relation to non-traded market risk (Interest Rate Risk, Credit Spread and FX Risk in the Banking Book) and any other requirements with respect to non-traded market risk.
The ALM team provides an independent risk oversight of liquidity and non-traded market risk, and monitors compliance with risk policies and risk appetite metrics. The ALM function reports to the IBP CRO. The team consists of 10 people with sub teams focusing on liquidity, non-traded market risk and regulatory reporting.
Key objectives:
Identify, quantify, monitor, manage non-traded market rate risks in the Banking Book using the most appropriate risk methodologies and techniques adopted by the ALM team and approved by Senior Management of the Group. Monitor non-traded market risk against internal triggers and limits as well as against regulatory limits. Analyse, design and propose changes or enhancements to existing non-traded market risk methodologies and key assumptions. Provide analytical support to ensure that non-traded market risk methodologies are developed and implemented accurately Perform technical analysis to substantiate, test and validate the methodologies and assumptions underpinning the NTMR models
Using Python & database queries skills, enhance the existing data manipulation processes within the model risk management. Further develop the analytics processes by validating their methodological relevance and tracking their modelled appropriateness. Undertake project work using risk models, analysing and confirming the results produced, testing new methodologies. Form close relationships with business areas and present and report the results at Group ALCO.
Core Skills and Knowledge:
Proven experience with scripting languages : Demonstrated proficiency in scripting languages, particularly Python, along with experience in visualisation tools such as PowerBI.
Data cleaning and analysis techniques : Strong understanding of data cleaning, transformation, and analysis techniques using Python libraries (e.g., Pandas, Polars, Scikit-learn, NumPy), along with familiarity in fundamental machine learning techniques including regression, classification, and clustering.
SQL proficiency : Skilled in SQL for querying databases and analysing large datasets, with the ability to develop robust and performant queries.
Version control and coding practices : Experience with version control systems and adherence to good coding practices, ideally within Azure/GitHub environments.
Cloud environment experience : Familiarity with working in cloud environments, preferably Azure, and utilising basic compute and data resources. Good understanding of the financial markets and instruments
Good financial product knowledge ideally retail and wholesale products Knowledge of IRRBB, CSRBB, FXRBB along with the various industry-accepted ALM risk techniques would be beneficial Excellent communication skills, both written and verbal Strong attention to detail/accuracy and capable of seeing the overall objective Systematic approach in analysing and solving problems Ability to meet tight dead-lines Driven and ambitious, willing to take on extra responsibility Creative team player and willing to work in a constantly changing environment