Senior Engineer, Risk Analytics

Standard Bank Group; Johannesburg; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Standard Bank Group seeks a Risk Analytics Engineer to operationalize risk models in production and deliver robust MLOps pipelines within a cross-functional squad. You will containerize and deploy models as secure, versioned APIs, monitor data drift and model health, and collaborate with data scientists, data engineers and platform engineers to ensure scalable, low-latency decisioning.

The role emphasizes automation, software engineering best practices, and strong Python development in a

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative field.
• 3–5+ years in DevOps, Machine Learning Operations, Software Engineering or Data Engineering with automation.
• Strong programming proficiency, particularly in Python; proven experience with CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).

Responsibilities

• Operationalize risk models in production environments.
• Design, build, and maintain automated CI/CD pipelines to test, validate, and deploy risk models and decisioning logic.
• Package and deploy models as secure, versioned APIs.
• Implement production monitoring for data drift and model health.
• Collaborate with Data Scientists, Data Engineers and Platform Engineers to ensure seamless production deployment.

Skills

Python

CI/CD tooling

GitHub Actions

Azure DevOps

Jenkins

Docker

Kubernetes

AWS

Azure

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

Terraform

CloudFormation

AWS

Azure

Job description

Location: ZA, undefined, Johannesburg, 30 Baker Street

Business Segment: Personal & Private Banking

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 automatedContinuous Integration and Continuous Delivery/Deployment pipelines to test, validate, and deploy risk models and decisioning logic.

Model Deployment & Serving: Package (containerize) and deployMachine 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/Deploymenttools (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 Codetools (Terraform, Cloud Formation)

Additional Information

• Adopting practical approaches
• Articulating Information
• Communication and collaboration skills
• Problem solving
• Managing Tasks
• 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.

Our Commitment to Diversity, Equity and Inclusion

Standard Bank Group is committed to fair and inclusive employment practices. We aim to build a workforce that reflects the diversity of the communities we serve and welcome applications from candidates of all backgrounds, including people with disabilities. We stride to create an environment where everyone has an equal opportunity to contribute and grow.