Data Scientist - Credit Eligibility

Key Responsibilities

• Model Development: Build and refine credit scoring models that assess customer creditworthiness, default risk, and loan pricing across multiple markets.
• Testing & Optimisation: Develop and test ML models for loan eligibility and pricing optimisation through A/B testing and statistical analysis.
• Performance Improvement: Continuously improve eligibility criteria by analysing repayment data, engineering new features, and monitoring credit performance for risk shifts and margin impact.
• Cross-functional Collaboration: Partner with engineers, data scientists, and commercial stakeholders to scale models into production.

Technical Environment

• Languages & Libraries: Python, SQL, scikit-learn, pandas, numpy, and relevant ML libraries.
• Techniques: Predictive modelling, classification/regression, feature engineering, model selection, hyperparameter tuning, A/B testing.
• Domain: Credit scoring, underwriting, loan pricing, risk analytics.

Experience & Requirements

• Experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems.
• Machine Learning (ML) background with hands-on experience in model development, validation, deployment, and performance monitoring.
• Proficiency in Python, SQL, and relevant ML libraries (scikit-learn, pandas, numpy, etc.) with experience in feature engineering, model selection, and hyperparameter tuning.
• Experience translating complex model outputs into actionable business strategies and stakeholder communications.
• Ability to work cross-functionally with product, engineering, and commercial teams.
• Strong data communication skills — written, oral, and visual.
• Degree qualification (BA/BSc/HND).

Highly Desirable

• Experience in credit, underwriting, lending analytics, or fintech modelling.

How to Apply

Interested and qualified candidates should apply online via the application page: Apply on Ashby via MyJobMag.