Machine Learning Engineer

Discovery Limited · Johannesburg, Gauteng

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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

Education and Experience

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