Summary:

You're responsible for developing and managing retail and non-retail collection scores, including customer-related risk models, to support risk decision-making and measurement. You ensure the appropriate use of credit risk models in alignment with company objectives while identifying new opportunities for digital lending. Additionally, you're expected to share knowledge and collaborate with the team to achieve objectives throughout all phases of the model lifecycle.

Job Description:

• Manage, clean, and prepare internal and external data (structured, semi-structured, and unstructured) for model development, deployment, and monitoring, including creating data quality and integrity reports
• Develop and enhance statistical, expert, and hybrid models using various data modeling techniques (e.g., Logistic Regression, Random Forest, Gradient Boosting, Non-Parametric Regression) and, if needed, collaborate closely with external consultants across the entire model lifecycle
• Generate prescriptive models to optimize risk and reward through interactive decision
• Deploy and maintain credit risk models in the Databricks platform, collection system, and credit decision engine
• Work closely with business users, credit approval officers, and relationship managers to align credit risk models with business strategy and digital lending risk assessment. Collaborate with IT and data engineers to ensure data availability and quality from various sources (both on-premise and cloud) for efficient model development.
• Monitor, assess, and improve credit risk models throughout their lifecycle to ensure their continued effectiveness
• Identify opportunities to develop alternative credit scores using partnership data.

Qualifications:

• Bachelor's or higher degree in Finance, Statistics, Mathematics, Economics, MIS, Engineering, Data Science, or related fields
• Minimum 1–2 years of experience in credit risk analytics, credit risk modeling, or credit scoring within retail banking, consumer finance, or any financial business
• Proficiency in machine learning, credit scoring, and data analytics using R, Python, PySpark, MATLAB, SPSS, SAS, or SQL
• Strong analytical mindset, critical thinking, data analytics, and programming skills
• Excellent project management and communication skills, with proficiency in written and spoken English
• Ability to work under pressure with a positive attitude and strong teamwork skills
• Experience in credit risk modeling, model monitoring, validation, or deployment is preferred; knowledge of Basel, IFRS 9, RAROC, stress testing, big data, data mining, digital leading, or fin-tech/start-up is a plus