Risk Modelling Senior Officer

A GLIMPSE ABOUT CRÉDIT AGRICOLE EGYPT:

Crédit Agricole Egypt Since its launch in 2006, Crédit Agricole Egypt has succeeded to become a strong player on the economic scene, positioning itself as the Reference European Bank in Egypt. Aiming to become a leading innovation-driven sustainable bank, Crédit Agricole Egypt is committed to bring its international expertise to the local market to serve its clients and drive the sustainable development in line with Egypt’s national priorities.

With a universal customer-focused model, Crédit Agricole Egypt is “a whole bank just for you” serving the entire spectrum of customers: individuals, SMEs, enterprises, Corporate and public sector. Prioritizing its customers’ best interests, Crédit Agricole Egypt presents an extensive range of services, tailored financial solutions and the sound advice, to accompany its clients in their walk of life. Awarded most innovative bank in Egypt, convenience and distinguished customer experience is Crédit Agricole Egypt’s endeavor to attain its customers’ satisfaction throughout its nationwide network of branches and its state-of-the-art fully-fledged platform of digital services. Furthermore, Crédit Agricole Egypt is in line with the world’s digital transformation, encouraging and implementing an innovative environment to exceed its customers’ expectations.

In addition, for being an active contributor to the local economy, the bank is committed to support the development of the Egyptian community and the respect of the environment.

Overview:

The successful candidate will work at the intersection of credit risk and data science, developing advanced predictive models and data-driven solutions. This role is ideal for candidates with strong data science backgrounds looking to apply their skills in a real-world financial context.

Key Responsibilities:

• Develop predictive credit risk models for retail and corporate portfolios, leveraging data-driven approaches.
• Build and enhance IFRS 9/Expected Credit Loss (ECL) models, including estimating Probability of Default (PD) based on customer behaviour and historical performance, and Loss Given Default.
• Design and implement application and behavioural scorecards using a combination of traditional statistical approaches and modern machine learning methods.
• Perform in-depth data analysis on large and complex datasets to uncover insights and identify key drivers of customer behaviour and portfolio risk.
• Develop econometric models to quantify the impact of external factors on credit portfolio performance.
• Provide guidance, mentorship, and technical support to junior modellers, ensuring adherence to best practices in modelling and documentation.
• Collaborate with cross-functional teams to support broader business needs.

Candidate Profile:

• Strong academic background in Data Science, Statistics, Mathematics, Computer Science, or related fields
• Proven experience in predictive modelling and advanced analytics.
• Proficiency in Python, R, and SQL.
• Knowledge of credit risk or banking is a plus, but not mandatory.
• Strong problem-solving mindset and ability to work with complex datasets.
• Ability to work effectively within a team environment, collaborating with diverse stakeholders.
• Excellent communication and presentation skills, with the ability to clearly explain complex technical concepts to non-technical audiences.