Υπάλληλος

Purpose

As a Risk Model Development Intern/ Junior Analyst, you will join the Credit Risk and Enterprise Risk Modeling Business Area of the Bank. You will contribute to the development and performance monitoring of models used in measuring and assessing market, counterparty and liquidity risk and interest rate risk in the banking book (IRRBB) at Group level. You will also support model-related submissions to the Bank’s and Group Companies’ competent Committees.

What you will be doing

• Development of behavioral models for IRRBB and Liquidity Risk in compliance with regulatory requirements and in line with industry best practices, working closely with the Bank’s business areas.
• Performance monitoring for market and counterparty risk models as well as behavioral models for IRRBB and Liquidity Risk.
• Prepare model outputs and documentation to support regulatory reporting and internal risk management, including ICAAP, ILAAP, and supervisory model reviews.
•  Conduct regular reviews and updates of model documentation, including   methodology guides, user manuals, and relevant policy documents.
• Research and development best practices in the application of time series analysis and econometric techniques to enhance risk model efficiency.
• Research of best practices in applying data analytics in risk modelling.

What you need to have

• A Bachelor’s degree in a quantitative field (e.g. Engineering, Statistics, Mathematics, Quantitative Finance, Computer Science, or related).
• A Master’s degree in any of the abovementioned disciplines.
• 1-3 years of professional experience in building and implementing models for market, counterparty and/ or liquidity risk and/ or interest rate risk in the banking book is preferred, although not a must.
• Solid experience with statistical packages, such as E-views or R-project.
• Experience in procedural and imperative programming languages (e.g. SQL, PL/SQL,T-SQL etc.) and use of databases is a plus.
• Strong coding skills in programming languages, such as Python, will be considered an asset.
• Familiarity with econometric time series techniques, such as Autoregressive Distributed Lag (ARDL) models, is a plus.
• Good understanding of machine learning, deep learning and NLP algorithms and methodologies is a plus.
• Excellent command of the English language (both written and spoken).