Quantitative Analyst: CCR DCEM Opportunity at Absa

Absa Group is seeking an analytical and detail-oriented Quantitative Analyst to join its Counterparty Credit Risk (CCR) team focusing on the Dynamic Credit Exposure Method (DCEM). Based in Sandton, this role is positioned within Absa Corporate and Investment Banking, playing an essential part in calculating, analysing, and managing counterparty credit risk across sophisticated derivative portfolios. The analyst will work at the intersection of mathematical modelling, financial market dynamics, and regulatory compliance.

As part of this high-performing risk management function, you will contribute directly to the measurement of Potential Future Exposure (PFE) and Mark-to-Market (MTM) profiles for over-the-counter (OTC) derivatives and Securities Financing Transactions (SFTs). You will collaborate closely with Front Office trading desks, Credit Risk sanctioners, Quantitative Analytics specialists, and Enterprise Technology teams to evaluate transaction structures and uphold robust institutional risk governance.

Key Responsibilities & Core Duties

• Calculate, analyse, and explain Potential Future Exposure (PFE) and Mark-to-Market (MTM) exposure metrics across OTC derivatives and SFT structures.
• Support Front Office traders and Credit Risk officers with pre-deal pricing calculations, ad hoc simulations, and credit limit utilisation queries.
• Assess the primary drivers of counterparty exposure at trade, netting-set, and broad portfolio levels, taking into account margining and collateral agreements.
• Perform comprehensive stress testing, sensitivity analysis, and scenario analysis to simulate the impact of extreme market shocks on portfolios.
• Assist in maintaining, back-testing, and enhancing internal risk models, QlikView dashboards, and ETL pipelines alongside quantitative developers.
• Prepare regular regulatory counterparty credit risk disclosures, prudential daily margin reports, and executive risk committee packs.

Ideal Candidate Profile & Core Attributes

• Strong mathematical, statistical, or quantitative background with solid exposure to derivative instruments and risk theory.
• Working familiarity with financial data systems, automated scripting, and reporting tools such as Python, SQL, or QlikView.
• Disciplined analytical reasoning with the ability to investigate complex data anomalies, reconcile differences, and explain findings clearly.
• Commitment to sound risk governance, regulatory compliance, and high standards of technical documentation.

Learning, Mentorship & Career Progression

Absa provides quantitative professionals with comprehensive exposure to tier-one investment banking operations. Working alongside seasoned financial engineers and credit risk specialists, you will deepen your mastery of SA-CCR frameworks, advanced pricing algorithms, and counterparty portfolio stress testing. Successful performance opens clear pathways toward senior quantitative analytics, structured credit risk, or front-office trading desk risk positions.

Estimated Salary Range (Market Benchmark)

The hiring employer has not officially specified a salary for this position. Based on South African market data and industry benchmarks for a corporate banking quantitative analyst role, the estimated remuneration is approximately R28,000 – R45,000 per month. Please note that this is an industry estimate and actual remuneration will be determined by the employer based on qualifications, experience, and internal pay structures.

How to Apply & Submission Guidelines

Interested candidates should apply online via the Absa careers portal. Please submit a comprehensive curriculum vitae accompanied by complete academic transcripts. Shortlisted applicants will participate in competency evaluations and technical quantitative assessments.

• Tertiary qualification in Quantitative Finance, Financial Engineering, Applied Mathematics, Statistics, Actuarial Science, or Physics.
• Solid foundational knowledge of derivatives pricing, financial markets, and counterparty exposure concepts (PFE, MTM, SA-CCR).
• Proficiency in quantitative programming and data analytics tools (e.g., Python, R, SQL, MATLAB, or QlikView).
• Strong numerical, written, and verbal communication skills with the ability to articulate technical risk models to diverse stakeholders.