Quantitative Credit Analyst

<p style="text-align:left"><span class="emphasis-2"><b>Let&#39;s Write Africa&#39;s Story Together!</b></span></p><p style="text-align:left"><span class="emphasis-2">Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this. </span></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><b>Job Description</b></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>Role details:<br>The Group Credit Investment (GCI) Quantitative Credit Analyst is a quantitative specialist embedded in the Credit Team, responsible for the development, maintenance and enhancement of internal credit rating scorecards. The role translates financial and non-financial risk drivers into practical, transparent and robust scoring frameworks that support consistent credit assessment and monitoring across the GCI investment credit portfolio.<br>Working closely with fundamental credit analysts, the incumbent contributes quantitative analysis where it can strengthen the assessment of a counterparty, transaction or credit risk question. This includes testing assumptions, analysing sensitivities and interpreting model outputs, with clear explanations of their implications and limitations. Quantitative tools inform, rather than replace, fundamental credit judgement<p style="text-align:inherit"></p><p style="text-align:inherit"></p><h1><b> Key Result Areas:</b></h1><p> </p><p><b>Rating Scorecard Development, Maintenance and Governance</b></p><ul><li>Take responsibility for developing, maintaining and enhancing internal credit rating scorecards, including redevelopment where existing methodologies require improvement.</li></ul><ul><li>Work with credit analysts to identify economically meaningful financial and non-financial risk drivers, assess their relevance across sectors and counterparty types, and translate them into appropriate scoring frameworks.</li></ul><ul><li>Evaluate variable selection, weightings, scoring thresholds and rating mappings using suitable quantitative methods and credit rationale. Calibrate models where data permits, explicitly addressing limited samples, missing data and the use of expert judgement.</li></ul><ul><li>Test scorecard performance, stability, sensitivity and consistency; undertake back-testing and benchmarking where appropriate; and investigate rating migrations, overrides and anomalous outputs.</li></ul><ul><li>Maintain clear methodology and technical documentation covering data sources, assumptions, calculations, testing, limitations, intended use and changes. Escalate material weaknesses and propose remediation.</li></ul><ul><li>Maintain controlled, reproducible scorecard implementations, with input checks, calculation testing, version control and traceable changes. Improve data quality and automate inputs and outputs where appropriate.</li><li>Prepare development validation andosals for the applicable review and approval processes, support independent validation, and address review findings</li></ul><br /><p><b>Targeted Quantitative Credit Analysis and Collaboration:</b></p><ul><li>Work with the Credit Team to define credit questions that warrant a quantitative approach, select proportionate methods and prioritise analytical work alongside the core scorecard mandate.</li></ul><ul><li>Develop targeted scenario and sensitivity analyses, peer comparisons and statistical assessments to test credit assumptions and identify risk drivers, vulnerabilities or emerging patterns.</li></ul><ul><li>Analyse financial statements and other relevant data as inputs to scorecards and quantitative assessments, checking data definitions, comparability, completeness and reliability.</li></ul><ul><li>Contribute specialist quantitative analysis to credit papers and committee discussions as required. Explain findings, uncertainty and limitations in decision-useful terms, while fundamental analysts retain responsibility for the overall credit assessment and transaction recommendation.</li></ul><ul><li>Support consistent scorecard application through practical guidance, user support and knowledge sharing. Investigate queries and challenge results that are inconsistent with the underlying credit evidence.</li></ul><ul><li>Collaborate with the Analytics Function on shared datasets, analytical methods and implementation tools, agreeing interfaces and avoiding duplication of work.</li></ul><br /><p><b>Qualifications, Skills and Experience:</b> </p><ul><li><span style="color:#000000">A degree with substantial quantitative content in statistics, mathematics, econometrics, actuarial science, engineering, quantitative finance, business science or an equivalent discipline.</span></li></ul><ul><li><span style="color:#000000">2-3 years of relevant experience</span></li></ul><ul><li><span style="color:#000000">Demonstrable applied experience in quantitative modelling, statistical analysis and working with financial data, including testing assumptions and communicating model limitations.</span></li></ul><ul><li><span style="color:#000000">Practical programming capability in Python, R or an equivalent analytical language, together with strong Excel and financial modelling skills. Experience with SQL, reproducible analytical workflows and version control would be advantageous.</span></li></ul><ul><li><span style="color:#000000">Sound understanding of financial statements, credit risk, investments and fixed income instruments, sufficient to develop and interpret economically meaningful credit models.</span></li></ul><ul><li><span style="color:#000000">Experience in credit rating scorecard development, calibration, performance monitoring, model documentation or implementation would be advantageous, particularly in corporate or investment credit and data-constrained environments.</span></li></ul><br /><p><span style="color:#000000">A relevant postgraduate qualification, CFA or CA(SA) would be advantageous where supported by demonstrable quantitative modelling and programming capability.</span></p><br /><p><b>Competencies:</b> </p><ul><li><span style="color:#000000">Strong quantitative reasoning and ability to select methods appropriate to the credit question and available evidence.</span></li></ul><ul><li><span style="color:#000000">Credit and commercial judgement connect model design and outputs to the underlying economics and practical investment context.</span></li></ul><ul><li><span style="color:#000000">Critical thinking and constructive challenge: tests assumptions, interrogates unexpected results and avoids false precision.</span></li></ul><ul><li><span style="color:#000000">Technical discipline and attention to detail in data preparation, coding, calculations, testing and documentation.</span></li></ul><ul><li><span style="color:#000000">Ownership and delivery independently progress scorecard development and maintenance, resolve issues and escalate material concerns promptly.</span></li></ul><ul><li><span style="color:#000000">Clear written and verbal communication, including the ability to explain quantitative findings and limitations to non-specialist stakeholders.</span></li></ul><ul><li><span style="color:#000000">Collaboration and service orientation, working effectively with fundamental credit analysts, the Analytics Function and model reviewers.</span></li></ul><ul><li><span style="color:#000000">Strong planning and prioritization, balancing ongoing scorecard responsibilities with targeted analytical requests and multiple deliverables.</span></li><li><span style="color:#000000">Professional integrity, respect for confidential information and adherence to model governance and approval boundaries.</span></li><li>Practical problem-solving, continuous learning and a focus on reliable, usable tools that improve credit decision-making.</li></ul><p> </p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><b>Skills</b></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>Action Planning, Analytics Software, Budget Management, Business Intelligence (BI) Analysis, Computer Literacy, Data Analysis, Data Compilation, Data Controls, Data Interpretations, Data Modeling, Evaluating Information, Numerical Aptitude, Report Review, Solution Analysis, Statistical Analysis Techniques<p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><b>Competencies</b></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>Business Insight<h3></h3><h3></h3>Cultivates Innovation<h3></h3><h3></h3>Manages Complexity<h3></h3><h3></h3>Optimizes Work Processes<h3></h3><h3></h3>Situational Adaptability<h3></h3><h3></h3>Strategic Mindset<h3></h3><h3></h3><h3></h3><h3></h3><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><b>Education</b></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent<p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><b>Closing Date</b></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>02 November 2026 , 23:59<p style="text-align:inherit"></p><p style="text-align:inherit"></p><p><i><b>The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.</b></i></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:left"><i><span class="emphasis-2"><b>The Old Mutual Story!</b></span></i></p>