Quantitative Analyst at Capitec Bank

• We’re looking for a senior Machine Learning and Data Science Analyst to independently validate high‑impact models across credit risk, financial crime, and advanced analytics use cases. 
• This is a hands‑on technical role.

What You'll Be Doing

• Leading the independent validation of machine learning models across: 
• Credit risk models
• Propensity and behavioural models
• Financial crime models (fraud and AML)
• Applying advanced ML techniques, including: 
• Supervised learning (Random Forest, XGBoost, CatBoost, Neural Networks)
• Unsupervised learning (clustering, isolation forests)
• Managing model risk across the end‑to‑end model lifecycle, including: 
• Feature engineering and data preparation
• Model training, evaluation, and selection
• Production deployment and monitoring
• Building and reviewing models in Python‑based environments 
• Leading and mentoring analysts and junior data scientists 
• Partnering closely with Risk, Technology, and Business stakeholders 
• Ensuring models meet governance, performance, and scalability standards

What We Are Looking For

• 6–8+ years relevant experience, with demonstrated technical leadership  
• Strong hands‑on experience building machine learning and data science models end‑to‑end 
• Proven use of techniques such as:  
• Boosting algorithms (XGBoost, CatBoost) 
• Neural networks 
• Clustering and anomaly detection 
• Advanced proficiency in Python 
• Solid experience with SQL and working with large, complex datasets 
• Ability to lead technically while remaining actively involved in modelling work 
• Experience within credit risk, propensity modelling, or financial crime analytics 
• Experience with independent validation of models and/or detailed peer review 
• Proven experience researching machine learning models 

Qualifications

• Honours or Master’s degree in Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative field 

Preferred/ Ideal

• Experience leading or building ML teams in a regulated environment 
• Experience deploying or supporting models in cloud environments 
• Exposure to credit risk modelling, scorecards, or IFRS‑related analytics 
• Financial crime (fraud or AML) modelling experience 
• Experience designing models with scalability and deployment in mind 
• Familiarity with model risk, governance, or validation standards