Senior Data Scientist

Overview

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 hands‑on technical role involves leading model validation, applying advanced ML techniques, and managing model risk across the full lifecycle.

• Lead the independent validation of machine learning models across
  • Credit risk models
  • Propensity and behavioural models
  • Financial crime models (fraud and AML)
• Apply advanced ML techniques including
  • Supervised learning (Random Forest, XGBoost, Cat Boost, Neural Networks)
  • Unsupervised learning (clustering, isolation forests)
• Manage model risk across the end‑to‑end lifecycle:
  • Feature engineering and data preparation
  • Model training, evaluation, and selection
  • Production deployment and monitoring
• Build and review models in Python‑based environments
• Lead and mentor analysts and junior data scientists
• Partner closely with Risk, Technology, and Business stakeholders
• Ensure models meet governance, performance, and scalability standards

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

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

• 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

• Work on high‑impact, real‑world models used across the bank
• Exposure to a wide variety of models, not just one product or portfolio
• Learn from experienced quantitative leaders in a collaborative environment
• Be part of a fast‑growing organisation that values simplicity, transparency, and ownership
• Competitive rewards, learning opportunities, and long‑term career growth

• Clear criminal and credit record

Capitec is committed to diversity, applications to this position will strictly be considered in support of our employment equity goals.