Senior Quantitative Analyst

About the Role

To translate advanced portfolio insights into practical business strategies that drive profitable growth, healthy credit outcomes, customer loyalty and retention across the Home Loans and Motor Finance Corporation portfolios. The role combines hands-on quantitative analysis, strategic thinking, stakeholder influence and people leadership to identify opportunities, mitigate emerging risks and support data-driven decisions across the secured lending value chain.

Responsibilities

• Analyse secured lending portfolio, profitability, customer behaviour and credit outcomes to identify emerging risks and commercial opportunities.
• Translate complex analysis into clear, practical recommendations for senior leadership and decision-making forums.
• Develop and evaluate portfolio strategies covering profitable growth, risk-adjusted returns, customer loyalty, retention, switch attack and switch defence.
• Assess campaigns, pricing and policy changes, business initiatives and portfolio optimisation opportunities through robust pre- and post-implementation analysis.
• Build accurate, complete and reproducible analyses, reporting and decision-support tools; automate recurring processes where appropriate.
• Partner across Secured Lending Leadership, Credit, Pricing, Acquisitions, Impairments, Collections, Finance, and Product.
• Lead, coach and develop one analyst while strengthening team capability through knowledge sharing, constructive review and on-the-job guidance.
• Operate with a high level of independence.
• Support the business strategy, values, transformation goals and continuous improvement agenda through credible analysis and effective stakeholder engagement.
• Increase efficiencies through programming and automating processes.
• Enhance business efficiencies and ensure compliance through formal communications and interacting with external bodies.
• Challenge model builds from around the cluster through peer review participation and ensuring best practice.
• Take corrective action where risk is identified in any processes; models or reporting; through analysis and formal communication.
• Improve personal capability and stay abreast of developments in field of expertise by identifying training courses and career progression for self through input and feedback from managers.
• Ensure personal growth and enable effectiveness in performance of roles and responsibilities by ensuring all learning activities are completed; experience practiced and certifications obtained and/or maintained within specified time frames.

Requirements

• Professional Qualifications/Honour’s Degree
• 5–7 years’ relevant quantitative or analytical experience
• Exposure to credit, portfolio management or risk analytics
• Experience using analytical tools such as SAS, Python or SQL
• Stakeholder management experience across various levels
• Strong track record of statistical modelling, forecasting, and quantitative analysis
• Proficiency in at least one programming language (Python, R, C++, Java, SQL, SAS, VBA, MATLAB), with the ability to design, translate, and implement mathematical or statistical models into flexible, scalable and production-ready code
• Technical understanding of risk management models (e.g. PD/LGD, VaR, Expected Shortfall, EPE/PFE)
• Technical understanding of accounting standards
• Exposure and technical understanding of the full model lifecycle
• Technical understanding of testing methods and types (e.g. stress testing, back-testing, regression, performance, system, and unit testing).
• Familiarity, exposure and technical understanding
• Exposure and Technical understanding to AI/ML techniques and model explainability tools (e.g. SHAP, LIME, etc.).
• Familiarity with cloud-based analytics platforms and deployment practices
• Applied Learning
• Coaching
• Communication
• Decision Making
• Continuous Improvement
• Quality Orientation
• Driving Execution
• Strategic Influence
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Preferred Qualifications

• Postgraduate degree in Statistics, Mathematics, Actuarial Science, Engineering, Economics or related quantitative field.
• Understanding of LLMs, generative AI and agentic AI is advantageous