Data Science Lead AI and Software Development George

Our client, a well established insurance and financial services group based in George, is seeking an experienced Data Science Lead to build and drive its AI and machine learning capability. This is a hands on leadership role in a young ambitious data organisation where you will remain personally close to the modelling work while mentoring a growing team. The role is permanent and based on site in George.

What you will do:

• Lead the design, development and production deployment of machine learning models that deliver measurable commercial value.
• Own the full model lifecycle end to end from business requirement through to production monitoring and retraining.
• Run business discovery sessions with stakeholders to scope opportunities, define success measures and prioritise the AI delivery roadmap.
• Build and maintain supervised, unsupervised and time series models for use cases like customer lifetime value, propensity, churn and fraud detection.
• Partner with Data Engineering and Analytics Engineering teams to ensure robust, well governed data pipelines.
• Implement MLOps practices and production deployment pipelines on a modern cloud native stack.
• Present model outputs and recommendations to executive and non technical audiences.
• Mentor and develop data scientists through pairing, code review and structured coaching.

This is a player coach role where you lead a team and stay in the work.

• At least five years experience in data science or machine learning, including a minimum of two years in a senior or lead capacity.
• Proven track record of personally delivering production machine learning models with measurable business impact.
• Strong Python and SQL skills, with familiarity in modern data warehousing.
• Hands on experience with supervised, unsupervised and time series modelling techniques.
• Experience owning the full model lifecycle from business requirement through to production.
• Experience with cloud data platforms such as Azure, GCP or AWS.
• Strong business engagement and stakeholder communication skills, including presenting to executive committees.
• Experience leading or mentoring data science teams while remaining technically hands on.
• Working awareness of model governance, explainability and regulatory requirements in a South African financial services context, including POPIA, FAIS and Treating Customers Fairly principles.
• Advantageous: insurance or financial services domain exposure, CLV, propensity, fraud or churn modelling, MLOps pipelines, Snowflake, dbt, Databricks, Azure ML, Vertex AI, BigQuery ML or generative AI frameworks.

• Lead the design, development and production deployment of machine learning models that deliver measurable commercial value.
• Own the full model lifecycle end to end from initial business requirement and discovery through to production monitoring and retraining.
• Run business discovery sessions with stakeholders to scope opportunities, define success measures and prioritise the AI delivery roadmap.
• Build and maintain supervised, unsupervised and time series models across use cases such as customer lifetime value, propensity, churn and fraud detection.
• Partner with Data Engineering and Analytics Engineering teams to ensure robust, well governed data pipelines supporting model development.
• Set and enforce technical standards for model quality, validation, documentation and peer review.
• Implement MLOps practices and production deployment pipelines on a modern cloud native stack.
• Ensure model governance, explainability and compliance with regulatory expectations, including POPIA, FAIS and Treating Customers Fairly principles.
• Present model outputs, insights and recommendations to executive and non technical business audiences in clear commercial language.
• Mentor and develop data scientists through pairing, code review and structured coaching while remaining personally hands on.
• Shape the broader AI strategy in partnership with senior leadership and contribute to capability planning as the team scales.
• Evaluate and introduce relevant innovations, including large language models, generative AI and agent frameworks, where they create practical business value.
• Monitor deployed models in production and report on performance, drift and realised business impact.