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

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).