Required Experience:

• Experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems
  
• Strong 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
  
• Highly Desirable:
  
• Experience in credit, underwriting, lending analytics, or fintech modelling
  

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
  
• Our Team Approach
  
• Low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact
  
• High degree of ownership over your domain — you’re empowered to make data-driven decisions and prioritise solutions
  
• Cross-functional collaboration with engineering, product, and commercial teams across multiple countries
  
• Analytical rigour combined with deep market understanding to serve customers excluded from formal financial services
  

• Building and refining credit scoring models that assess customer creditworthiness, default risk, and loan pricing across multiple markets
  
• Developing and testing ML models for loan eligibility and pricing optimisation through A/B testing and statistical analysis
  
• Continuously improving eligibility criteria by analysing repayment data, engineering new features, and monitoring credit performance for risk shifts and margin impact
  
• Collaborating cross-functionally with engineers, data scientists, and commercial stakeholders to scale models into production