REQUISISTION DETAILS

REQ 146230 Thembi Mtshali

Location: Johannesburg

Closing Date: 10 June 2026

Job Family

Career Stream

Leadership Pipeline

Job Purpose

To design, develop, and deploy advanced analytical and quantitative solutions that strengthen Non-Financial Risk (NFR) management across the organisation. This includes enabling proactive risk identification, measurement, monitoring, and decision-making across areas such as financial crime analytics (Fraud and Anti Money Laundering), conduct risk, and model risk.

The role combines data science, quantitative analytics, and risk expertise to deliver scalable, regulatory-aligned solutions that enhance risk visibility and resilience.

Job Responsibilities

NFR Analytics and Risk Modelling 
Develop and maintain robust analytical models and frameworks to quantify and monitor non-financial risks, including fraud and financial crime indicators, compliance and conduct risks, and cyber and data risk metrics.

• Apply advanced statistical and machine learning techniques to identify patterns, anomalies, and emerging risks, and supporting the development of early warning indicators, key risk indicators (KRIs), and risk scoring models.


• Perform scenario analysis, stress testing, and trend analysis to proactively anticipate and manage potential risk exposures.

Data Engineering and Risk Data Management 

• Source, integrate and prepare both internal and external datasets, including incident data, control assessments, audit findings, and transactional signals, to support risk analytics initiatives.


• Ensure high standards of data quality, completeness, and traceability, enabling reliable and auditable risk insights.


• Build and structure datasets that are fit for advanced analytics and predictive modelling, while collaborating closely with technology teams to support the development and optimisation of scalable risk data pipelines.

Risk Monitoring and Insight Generation

• Continuously monitor key risk indicators to identify emerging trends, anomalies, and potential areas of concern.


• Translate analytical outputs into clear, actionable insights that support risk, compliance, and broader business stakeholders in informed decision-making.


• Develop and maintain dashboards and visualisations to enable real-time visibility of risk exposure, and responding to ad hoc risk analysis requests and supporting investigations as required.

Governance, Compliance and Risk Framework Alignment 

• Ensure that all analytics activities are fully aligned with internal non-financial risk (NFR) frameworks, policies, and governance standards.


• Support regulatory compliance by embedding relevant requirements—such as anti-money laundering (AML) and conduct monitoring—into analytical processes where applicable. 


• Comprehensive documentation of models, methodologies, assumptions, and data lineage to meet audit and regulatory expectations, while assisting in the timely remediation of findings arising from audit, model validation, or regulatory reviews.

Business Engagement and Risk Advisory 

• Actively engage with risk, compliance, audit, and business teams to understand key risk challenges and translate them into effective, data-driven analytical solutions.


• Bridge the gap between technical analysis and risk management decision-making, ensuring that insights are both meaningful and actionable.


• Contribute to shaping risk strategy through informed analytical input, while supporting risk committees, forums, and reporting processes.


• Identify opportunities to enhance non-financial risk (NFR) analytics, improve control effectiveness, and evolve risk frameworks by driving the adoption of advanced analytical techniques such as AI and machine learning for improved risk detection and monitoring,


• Stay abreast of emerging NFR trends such as cyber threats and regulatory developments and promoting automation and efficiency in risk reporting and analytical processes.

Job Responsibilities Continue

Innovation and Continuous Improvement

• Identify and drive opportunities to enhance non-financial risk (NFR) analytics, improve control effectiveness, and strengthen risk frameworks.


• Contribute to the adoption of advanced analytical approaches, including artificial intelligence and machine learning, to improve risk detection and monitoring capabilities. 


• Stay up to date with emerging NFR trends, such as evolving cyber threats, regulatory developments, and advances in conduct risk analytics. 


• Promote automation and improve efficiency across risk reporting and analytical processes.

Collaboration and Capability Building 

• Effective partnership across cross-functional teams, to deliver integrated and impactful risk analytics solutions.


• Active participation in peer reviews and knowledge-sharing initiatives to strengthen the broader analytics community and promote best practices.


• Contribute to building and enhancing the organisation’s non-financial risk (NFR) analytics capability, while supporting the development of talent by mentoring and guiding junior analysts, where required.

Qualifications

Essential

Bachelor’s degree in a quantitative or analytical field:

Mathematics, Statistics, Data Science, Computer Science, Engineering, Actuarial Science, or similar

Preferred

Postgraduate degree or certifications in:

Risk Management (e.g., FRM)

Data Science / Machine Learning

Compliance or Financial Crime Analytics

Minimum Experience Level

1 – 3 years’ experience in analytics, risk, or data environments

Exposure to risk management, auditing, compliance, or operational processes is advantageous

Experience with data analysis, modelling, or research (including academic work)

Programming & Tools

• Proficiency in:
  
  
  
  • Python, SQL (preferred) or R
  
  
  
  


• Familiarity with:
  
  
  
  • Data visualisation tools (e.g., Power BI, Tableau)
  
  
  • SAS / VBA / other analytics tools advantageous
  
  
  
  


• Advanced Excel skills

Strong foundation in:

• 
  
  
  
  • Statistics and probability
  
  
  • Machine learning (basic to intermediate)
  
  
  • Anomaly detection, classification, and predictive modelling
  
  
  
  


• Ability to translate analytical techniques into scalable solutions

• 
  
  
  
  • Data structures and databases
  
  
  • Data extraction, transformation, and validation
  
  
  • Risk data aggregation and reporting principles
  
  
  
  

• Exposure to:
  
  
  
  • Model lifecycle management
  
  
  • Validation and monitoring practices
  
  
  • Documentation and audit requirements
  
  
  
  

Technical / Professional Knowledge

Behavioural Competencies