Quantitative Analyst

• Location: Santon JHB
  
• Employment Type: Permanent
  
• Work Model: Hybrid
  

CodeConnect Staffing is recruiting on behalf of a well-established financial services and banking organisation seeking a skilled Quantitative Analyst to join its team.

The ideal candidate will have a strong quantitative background with at least 2 years experience in credit risk analytics portfolio management or a related field. They should possess hands-on experience with analytical tools such as SAS Python R and SQL together with an understanding of credit modelling risk-based pricing and portfolio performance analysis.

This role focuses on leveraging advanced analytics statistical modelling machine learning and automation to optimise credit portfolio performance improve pricing strategies and support risk-adjusted profitability. The successful candidate will contribute to innovative analytical solutions that support strategic decision-making and digital transformation initiatives.

• Develop and maintain advanced analytical frameworks to support credit portfolio management and performance optimisation.
  
• Apply statistical modelling machine learning and predictive analytics to identify trends risks and opportunities across the credit lifecycle.
  
• Contribute to the development and enhancement of credit origination models methodologies and strategies.
  
• Conduct in-depth portfolio analysis to identify performance drivers emerging risks and opportunities for portfolio rebalancing.
  
• Analyse risk-based pricing strategies identifying opportunities to optimise pricing and improve risk-adjusted returns.
  
• Evaluate model performance by comparing predicted outcomes against actual portfolio results and profitability.
  
• Support the development of client-level pricing frameworks that balance customer value credit risk and profitability.
  
• Conduct customer behaviour and cross-sell analytics to identify growth opportunities and optimise customer value.
  
• Develop interactive dashboards and data visualisations to support portfolio monitoring and reporting.
  
• Research and evaluate advanced analytical techniques to improve modelling accuracy efficiency and performance.
  
• Collaborate with internal stakeholders to integrate analytical insights into business strategies and risk appetite frameworks.
  
• Contribute to continuous improvement knowledge sharing and innovation within the analytics team.
  

• Bachelors degree in Mathematics Statistics Financial Engineering Economics Actuarial Science or a related quantitative discipline.
  
• Minimum 2 years relevant experience in credit risk analytics portfolio management or a related analytical environment.
  
• Proven experience working with SAS Python R and SQL.
  
• Familiarity with IFRS 9 Basel regulatory frameworks and advanced credit modelling techniques.
  
• Strong analytical statistical and problem-solving capabilities.
  
• Ability to interpret complex datasets and translate findings into meaningful business insights.
  
• Excellent communication and stakeholder engagement skills.
  

• Postgraduate qualification in a relevant quantitative discipline.
  
• Certifications in Data Science Machine Learning Financial Risk Management (FRM) or Chartered Financial Analyst (CFA).
  
• Experience applying machine learning methodologies to credit risk pricing or portfolio analytics.
  
• Exposure to advanced pricing models predictive analytics and data visualisation tools.
  

• Quantitative Analysis & Statistical Modelling
  
• Credit Risk & Portfolio Analytics
  
• Python SAS R & SQL
  
• Machine Learning & Predictive Analytics
  
• Risk-Based Pricing & Profitability Analysis
  
• Analytical Problem-Solving
  
• Business Acumen & Strategic Thinking
  
• Stakeholder Engagement & Communication
  
• Adaptability & Continuous Learning
  
• Results Orientation & Continuous Improvement
  

Candidates who meet the above requirements are encouraged to apply through the designated application link.

Please note: If you have not received feedback within two weeks of submitting your application please consider your application unsuccessful.