Quantitative Analyst

About the Role

REQ: 148018 Thembi Mtshali

Responsibilities

• Maintain advanced analytics frameworks for credit portfolio management.
• Contribute to enhancing traditional modelling approaches and apply machine learning methodologies to deliver predictive and prescriptive insights across the credit lifecycle.
• Research, evaluate and assess innovative analytical techniques and methodologies to enhance model development, performance and efficiency.
• Contribute to the development of origination modelling strategies and methodology to align with portfolio objectives and risk appetite.
• Assist in deep dive analysis into credit portfolios to identify performance drivers, emerging risks and opportunities for strategic portfolio rebalancing.
• Build interactive dashboards and visualisation tools for real-time portfolio monitoring.
• Conduct pricing analytics to assess the effectiveness of risk-based pricing strategies, assess optimisation opportunities and risk-adjusted returns.
• Compare model-predicted outcomes against actual portfolio performance to evaluate pricing models, assumptions and realised profitability.
• Contribute to the development and enhancement of client-level pricing frameworks to support holistic relationship-based pricing, balancing customer value, risk and profitability across the client portfolio.
• Support cross-sell and customer behaviour analytics that will inform and identify growth opportunities and optimise customer value.
• Collaborate with stakeholders to integrate analytics into strategic planning and risk appetite frameworks.
• Contribute to a collaborative and high-performance team environment through sharing knowledge, best practices and insights supporting innovation, continuous learning and professional development.

Requirements

• 2+ years in credit risk analytics, portfolio management or related fields.
• Proven experience with SAS, Python, R and SQL analytics platforms.
• Familiarity with IFRS9, Basel frameworks and advanced credit modelling techniques.
• Strong analytical and problem-solving skills.
• Strong communication and stakeholder engagement.
• Curiosity and a continuous learning mindset.
• Adaptability
• Applied Learning
• Communication
• Continuous Learning
• Continuous Improvement
• Driving for Results
• Influencing
• Business Acumen
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Preferred Qualifications

• Degree in Mathematics, Statistics, Financial Engineering, Economics, Actuarial Science, or related quantitative field.
• Postgraduate qualification or certifications in Data Science, Machine Learning or FRM/CFA advantageous.