Job Description

Are you an expert in credit risk modelling with a research-driven mindset and a track record of developing robust scoring models? Do you excel in environments where technical excellence, analytical rigour, and innovative thinking drive credit decisions?
We are partnering with a leading financial services group to appoint a Credit Risk Modeller focused on building and refining credit scoring models using both traditional and alternative data sources. This is your opportunity to shape credit vetting decisions across the customer lifecycle within an analytics-led, forward-thinking environment.
Key Responsibilities:

• Design, develop, and implement credit scoring models (PD, LGD, IFRS 9) using structured and unstructured/alternative data.
• Build machine learning and statistical models to enhance credit vetting and decisioning.
• Perform data mining, feature engineering, and statistical testing across large datasets.
• Collaborate with risk, credit, analytics, and technology teams to ensure seamless model integration and optimal performance.
• Document and publish model methodologies in line with governance and regulatory frameworks.
• Engage with internal stakeholders and committees, championing best practices in credit modelling across the business.

Ideal Background:

• MSc/PhD in Actuarial Science, Data Science, Quantitative Risk, or a related field.
• 5+ years of hands-on credit risk modelling experience within banking or financial services.
• Solid grounding in GLMs, survival analysis, Markov models, or Cox-regression frameworks.
• Proficiency in R, Python, SQL, and tools like SAS or Emblem.
• Experience with R Shiny, dashboards, or model explainability frameworks will be advantageous.
• Demonstrated experience in technical model development, with a track record of research contributions or involvement in academic/industry projects.

What Sets This Role Apart:

• Shape and drive the modelling roadmap across diverse credit portfolios.
• Join a high-performing team known for its innovation in IFRS 9 and survival modelling.
• Collaborate with credit risk, data science, and analytics specialists in a high-impact environment.
• Access cutting-edge research, alternative data strategies, and live model deployment.
• Flexible hybrid work setup with opportunities for career progression and thought leadership.

How to Apply:
If you meet the above requirements, please send your resume DIRECTLY to:
For more finance jobs, please visit our website: />
If there is no reply within two weeks, please assume that your application for the vacancy was unsuccessful. Your profile will be kept on our database for any other suitable roles / positions. We also invite you to reach out to us to discuss your next finance career opportunity!