Machine Learning Engineer

Key Purpose

• This ML Engineer is responsible for designing, building, managing, and continuously improving the operational processes that support the deployment and maintenance of actuarial, machine learning, and other decision-support models. The role involves end-to-end project planning, cross-functional collaboration with actuarial, data science, analytics, and business teams, and delivering insights into process efficiency across business areas. It also includes reporting on the design, progress, and performance of models and related business processes to drive operational excellence and informed decision-making.

Areas of responsibility may include but are not limited to

• Design and create implementation and testing processes for models in both a traditional and ML framework to ensure business decisions can be actioned quickly and effectively.
• Assist with the design and integration of traditional models and processes into cloud-based platforms like DataBricks to utilise additional functions and better performance.
• Responsible for deploying rating and logic engines using proprietary software, ensuring accurate implementation and seamless integration into production environments.
• Project planning and collaboration with actuarial, data science, underwriting, operational and system teams to ensure a smooth implementation process that reduces risks and achieves the required outcomes.
• Frequent monitoring and reporting on the progress and performance of existing models and processes, as well as presenting the design for new processes to upper management.
• Creation of automated processes and reports to reduce manual intervention and flag any areas of concern as they arise.
• Assessment of the efficiency and effectiveness of business processes through data analytics to identify any areas for improvement or cost savings.
• Implementation of pricing changes in existing models on a frequent basis, to ensurethat changes in our pricing structure and strategies can be quickly actioned.
• Creation of new tools and processes that can be used to reduce manual intervention and turnaround time of our client support teams.

Skills and Knowledge:

• Modelling skills preferred (Basic)
• Programming Skills: SQL, Python (Intermediate)
• Microsoft Office (Excel, PowerPoint and Word) (Advanced)

Education and Experience

Education:

• Matric (Essential)
• Honours degree in Actuarial Science and/or Mathematical Statistics/ Computer Science or Data Science (Essential)
• Min 3-5 completed CT subjects if Actuarial Degree (Advantageous)

Minimum Experience:

• At least 1-3 years’ experience within a data driven industry
• Experience with Databricks
• Experience with Azure ML solutions
• Experience with WTW Software (e.g., Radar Live) (advantageous)