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Quantitative Analyst

Job in Sandton, 2172, South Africa
Listing for: Afrizan Personnel
Full Time position
Listed on 2026-01-23
Job specializations:
  • Finance & Banking
    Data Scientist
  • IT/Tech
    Data Analyst, Data Scientist
Job Description & How to Apply Below

Job Description

Are you a quantitative thinker who thrives on turning complex credit data into powerful, commercial insight?

A leading financial services client is seeking a Quantitative Analyst – Credit Portfolio Analytics to join a high-impact Group Risk Analytics team. This role offers the opportunity to work at the forefront of advanced analytics and machine learning, shaping credit strategy and optimising risk-adjusted returns within a modern, hybrid working environment based in Sandton, Johannesburg.

Key duties

  • Develop and maintain advanced credit portfolio analytics frameworks
  • Apply machine learning and advanced statistical techniques to improve predictive accuracy
  • Perform in-depth portfolio analysis, including RWA and Economic Capital attribution and optimisation
  • Design dashboards and visualisation tools for real-time portfolio monitoring
  • Partner with stakeholders to embed analytics into risk appetite and strategic decision-making
  • Contribute to innovation, knowledge sharing and continuous improvement initiatives
  • Key requirements

  • Degree in Mathematics, Statistics, Financial Engineering, Economics, Actuarial Science or a related quantitative field
  • Postgraduate qualification or certifications in Data Science, Machine Learning, FRM or CFA (advantageous)
  • At least two years’ experience in credit risk analytics, quantitative modelling or a similar environment
  • Strong proficiency in SAS, Python, R and SQL
  • Working knowledge of IFRS 9, Basel frameworks and advanced credit modelling techniques
  • Excellent analytical ability, problem-solving skills and stakeholder communication
  • EE Disclaimer:
    All positions will be filled in accordance with the company's Employment Equity plan. We encourage people with disabilities to apply.

    Application Unsuccessful Disclaimer:
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