Credit Support Analyst

Main Purpose:

• Data engineering - Source data from internal system sources and external (public/private) sources, using data pipelines or automations
• Data science & risk modelling - Develop risk models / algorithm for calculating credit scoring of clients, credit loss, simulating stress scenarios and LLM models to aide credit risk assessment
• Risk Reporting & Visualization - Develop risk reports and dashboards for credit risk officers to analyse and monitor risks in their portfolio
• Data governance - Organise and document data attributes, process and procedures
• Infrastructure management - Collaborate with internal technology teams to analyse impact on data due to infrastructure changes
• To understanding trade Life Cycle – work closely with key departments across the organization, including Accounting, Trade Finance, Operations, and Deal Desk (amongst others) to understand data throughout the trade life cycle

Knowledge Skills and Abilities, Key Responsibilities:

• Bachelor’s degree in computer science, Finance, Mathematics, Statistics, or related field
• 3-5 years of experience in data engineering, data science, or similar technical role
• Proficiency in SQL and database management systems
• Strong programming skills in Python, R, or similar languages for statistical analysis and machine learning
• Experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch)
• Knowledge of statistical modeling, econometrics, and risk management methodologies
• Experience with ETL/ELT tools and data pipeline frameworks
• Familiarity with Large Language Models and Natural Language Processing techniques
• Experience with data visualization tools (Tableau, Power BI, or similar)
• Excellent English verbal and written communication

Key Responsibilities:

Data Engineering:

• Design, build, and maintain robust data pipelines to source data from internal system sources and external public/private data providers
• Develop automated data ingestion processes to ensure timely and accurate data flow
• Implement data quality controls and monitoring systems to maintain data integrity

Data Science & Risk Modelling:

• Develop and maintain sophisticated risk models and algorithms for calculating client credit scores
• Build predictive models for credit loss estimation and expected credit loss calculations
• Design and implement stress testing scenarios to evaluate portfolio resilience under adverse conditions
• Create and deploy Large Language Model (LLM) solutions to enhance credit risk assessment processes

Risk Reporting & Visualization:

• Develop comprehensive risk reports and interactive dashboards for credit risk officers
• Create visualization tools that enable effective portfolio risk analysis and monitoring

Data governance:  

• Organise and document data attributes, process and procedures

Infrastructure management:  

• Collaborate with internal technology teams to analyse impact on data due to infrastructure changes

Trade Life Cycle:

• Work closely with key departments across the organization, including Accounting, Trade Finance, Operations, and Deal Desk (amongst others) to understand data throughout the trade life cycle

Key Relationships          

Global Credit team, risk technology, data scientist, Other internal departments (accounts, treasury, trade finance, operations etc)

Key Relationships and Department Overview:

Global Credit team,  Head of Operation Risk Management, Other internal departments (accounts, treasury, trade finance, operations etc)