Data Science Lead (AI/ Software Development)

Job Description
Overview 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 the successful candidate will remain personally close to the modelling work while mentoring a growing team. The role is permanent and based on site in George. Key Responsibilities • 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. Requirements • 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. • 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. Benefits and Compensation Our client is offering a salary of R 75 000 to R 85 000 per month, depending on experience and qualifications. • A genuine player-coach leadership opportunity where you lead a team and stay in the work. • Full ownership of AI initiatives that directly influence how the business grows, prices and retains customers. • The opportunity to shape the AI strategy and delivery roadmap alongside senior leadership. • Work with a modern, cloud-native data stack including Azure, Snowflake, Python, dbt and GCP. • Scope to build a team culture and technical standard that becomes the benchmark across the group. • Clear growth path into a broader AI leadership role as the capability and team scale.
Requirements
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. 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.