Senior Engineer, Risk Analytics

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector’s most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. We’re passionate about creating growth in Africa. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you.

As a Risk Analytics Engineer, you are the critical bridge between advanced analytics and our production environment. You will be embedded within a cross-functional squad, responsible for the operationalization of risk models and strategies. Your primary mission is to ensure that the analytical solutions built by our data scientists and risk analysts—from credit scorecards to real-time fraud models—are deployed, monitored, and managed in a robust, automated, and scalable fashion. You will build and own the Machine Learning Operations pipelines and solutions that bring our risk intelligence to life.

Machine Learning Operations Pipeline Development (Continuous Integration and Continuous Delivery/Deployment): Design, build, and maintain automated Continuous Integration and Continuous Delivery/Deployment pipelines to test, validate, and deploy risk models and decisioning logic.

Model Deployment & Serving: Package (containerize) and deploy Machine Learning models and analytical engines as secure, versioned, and low-latency APIs, creating our "Risk-as-a-Service" capability.

Production Monitoring: Implement and manage comprehensive monitoring solutions for deployed models, tracking data drift, model performance degradation, and technical health (latency, errors).

Automation of Strategy: Work with Decisioning Configuration Analysts to automate the deployment and testing of business rules and strategies.

Collaboration & Enablement: Work side-by-side with Data Scientists to refactor and optimize their code for production. Collaborate with Data Engineers and Platform Engineers to ensure seamless integration and performance.

Tooling & Best Practices: Champion software engineering best practices within the risk analytics team. Contribute to the evolution of our Machine Learning Operations competency.

Qualification: 

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative field.

Experience Required

• 3-5+ years experience in the relevant technical role such as DevOps Engineer, Machine Learning Operations Engineer, Software Engineer or Data Engineer with focus on automation.
• Strong programming proficiency, particularly in Python.
• Proven experience with Continuous Integration and Continuous Delivery/Deployment tools (e.g. Github actions, Azure DevOps, Jenkins)
• Hands-on experience with cloud platforms (AWS or Azure)
• Experience with containerisation technologies and distributed computing (Docker, Kubernetes)
• Familiarity with Infrastructure as Code tools (Terraform, Cloud Formation)

Behavioural Competencies:

• Adopting practical approaches
• Articulating Information
• Communication and collaboration skills
• Problem solving
• Attention to detail
• Managing Tasks
• Output driven

Technical Competencies:

• Strong capability in modern data and Machine Learning operations, including orchestrating workflows, managing model lifecycles, and handling large‑scale data processing.
• Solid understanding of risk analytics within financial‑services or other regulated environments.
• Ability to integrate and operationalize models developed across diverse analytical and statistical toolsets.
• Practical experience implementing advanced AI solutions, including large‑scale language models, retrieval‑based architectures, and vector‑driven search.