Active Jobs at Standard Bank Group

Job Description

• To supply a fleet management service to customers (mainstream and government) by ensuring that on a national basis, all requests are actioned on time, that the administration processes are streamlined nationally and that all staff adhere to policies and procedures.
• To provide effective back-up service and functions to all Key Account Consultants, Account Managers, Business Development Officers and Business Development Managers.

Qualifications

Minimum Qualifications

• Diploma in Business Commerce or Office Administration

Experience Required

• 3-4 years Experience in managing an operations capability
• Understanding of Fleet processes and procedures

Additional Information

Behavioural Competencies:

• Challenging Ideas
• Embracing Change
• Empowering Individuals
• Generating Ideas
• Interacting with People

Technical Competencies:

• Business Administration Skills
• Data Management (Administration)
• Diary Management
• Email Monitoring
• Records and Archive Management

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Job Description

• 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.

Qualifications

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)

Additional Information

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.