Data Scientist - Credit Risk

SUMMARY:
This is a high-impact role responsible for driving the development of credit risk scorecards and underwriting models using Python and machine learning.

POSITION INFO:

Data Scientist - Credit Risk

Shape the Future of Credit Analytics in a Fast-Paced, High-Impact Role

Data Science Hybrid (Cape Town CBD) Market Related UK Working Hours (9 - 5)

About Our Client

Our client is an agile, data-driven financial services company with a strong focus on credit innovation. They operate in a fast-paced environment, leveraging advanced analytics and machine learning to deliver scalable solutions. With a culture centered on impact, strategic thinking, and hands-on problem solving, this is an ideal opportunity to shape the analytics function of a business where your work truly makes a difference.

The Role: Data Scientist - Credit Risk

This is a high-impact role responsible for driving the development of credit risk scorecards and underwriting models using Python and machine learning. You''ll take ownership of analytics initiatives, champion innovation in risk modelling, and apply your technical and business expertise to influence decision-making across the organization.

Key Responsibilities

• Design and develop credit risk scorecards and expected loss models
• Build, deploy, and monitor robust credit risk models using Python and ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow)
• Develop underwriting innovation using AI and data-driven techniques
• Maintain best practices in coding (including Git and version control), model deployment, and reproducibility
• Lead and scale the development environment and tooling to ensure reliability and collaboration
• Collaborate across teams to implement analytics solutions that drive business performance

About You

• 4- 5 years of experience in data science with a focus on credit risk modeling
• Hands-on experience building scorecards and risk models (non-mortgage)
• Strong Python programming skills and experience with ML libraries
• Background in a non-bank lender or dynamic financial services environment preferred (e.g., Lulalend, Retail Capital, Transaction Capital, Bayport, Capitec)
• Degree in a quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Engineering)
• Experience with AWS, Git, and AI tools is a plus
• Strategic thinker with a self-driven attitude and strong business acumen
• Comfortable working in a fast-paced, high-autonomy setting