Data Science Lead | George, Western Cape | On-site

Badger Holdings is a specialised insurance and related services company. Founded in 1995, we currently employ over 700 staff members across South Africa and Australia and insure over 180 000 clients with premiums in excess of US$100 million. Through a unique blend of skills and expertise, Badger Holdings has grown into a formidable force in the insurance landscape. We have been helping insurance and related companies to maximise their potential for almost two decades. Our success lies in establishing innovative insurance entities and partnering with great people who have excellent track records. We give them the means to grow sustainably into the future by providing expert assistance and advice on legal matters, training needs, marketing requirements and new product development. We believe that it is through partnership that companies will reach their full potential and achieve successful growth. That is why Badger Holdings embraces and encourages opportunities to establish firm, committed and longstanding business relationships. Our chairman often tells the story that inspired his vision for the group – about his friend who was about to crash in a glider plane. He prayed to be saved and felt a gentle wind lift the wings of the plane and gently help to land him. We see relationships and gentle landings in times of need as our offering.What you'll do As our Data Science Lead, you'll lead a team of three Data Scientists and one Machine Learning Engineer while driving strategic AI initiatives across the business. You'll: Lead the end-to-end delivery of machine learning and AI projects from business discovery through deployment and monitoring. Build, validate and deploy predictive models across customer lifetime value (CLV), fraud detection, lapse prediction, pricing optimisation, claims prediction and customer segmentation. Work closely with stakeholders across underwriting, claims, finance and operations to identify high-value opportunities. Translate commercial challenges into scalable data science solutions. Ensure models are production-ready, explainable, governed and deliver measurable business value. Present insights and recommendations to senior leadership and executive stakeholders. Mentor, coach and develop the Data Science team while establishing technical best practices. Collaborate with Data Engineering, Analytics Engineering and ML Engineering to deliver robust AI solutions. Contribute to ARC's evolving AI ecosystem across Azure, GCP and modern cloud data platforms. What we're looking for You'll thrive in this role if you combine technical excellence with strong leadership and commercial thinking. Requirements Qualifications Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative discipline. A postgraduate qualification in a relevant field is advantageous. Relevant cloud, AI or machine learning certifications (Azure, Google Cloud, AWS, Databricks or Snowflake) will be advantageous. Essential experience 5+ years' experience in Data Science, Machine Learning or Artificial Intelligence. Previous experience leading or mentoring Data Science teams. Proven success delivering production machine learning models with measurable business impact. Strong Python and SQL skills. Experience across supervised, unsupervised and time-series modelling techniques. Experience owning the full machine learning lifecycle from problem definition to production deployment. Strong stakeholder engagement and business partnering skills. Experience presenting technical concepts to senior leadership and non-technical audiences. Knowledge of cloud platforms such as Azure, AWS or Google Cloud Platform (GCP). Understanding of model governance, explainability and responsible AI. Highly advantageous Insurance or Financial Services experience. Experience with fraud detection, customer propensity, churn or CLV modelling. MLOps and production deployment pipelines. Snowflake, dbt or modern cloud data platforms. Azure ML, Vertex AI, BigQuery ML or Databricks. Exposure to Generative AI, Large Language Models (LLMs) or AI Agent frameworks. Experience working within regulated environments (POPIA, FAIS, TCF).