Data Scientist III at FNB South Africa

Job Description

• To plan, build, optimise and implement innovative quantitative analytical methodologies, procedures, products and advanced
• mathematical models that provide analytical support and interpret insights, using advanced analytics technologies, to address business opportunities and problems and implement business strategy.
• Own the end-to-end data science strategy for retention initiatives across all FNB Life insurance products. 
• Use data to understand drivers of policy lapses and churn, and design targeted interventions. 
• Develop and implement predictive models and decision frameworks to identify at-risk customers and recommend proactive retention actions. 
• Partner with marketing, product, and operations teams to execute data-driven campaigns and experiments. 
• Lead hypothesis testing and A/B testing to measure effectiveness of retention strategies. 
• Translate complex data science outputs into clear business language for stakeholders at all levels. 
• Track and report on key retention metrics and continuously improve strategies based on results. 
• Build and mentor a team of data scientists focused on retention analytics. 
• Ensure ethical data usage, adherence to privacy standards, and model governance policies. 
• Productionise analytics in approved FirstRand architecture and support operational implementation of models. 
• Collaborate across departments to ensure the effective use of data in solving business challenges. 
• Conduct data wrangling, text analytics, and visualisation to extract and communicate insights. 

​Requirements:

• Honours or master’s degree in data science, Statistics, Mathematics, Actuarial Science, Computer Science, Engineering, or a related quantitative field. 
• 5+ years of hands-on experience solving business problems using data, including project leadership. 
• Experience using SAS, Python, SQL, and cloud platforms (AWS, Azure, or GCP). 
• Big data project experience or exposure is advantageous. 
• Strong modelling experience with a proven ability to translate models into implemented business solutions. 
• Experience in customer analytics, churn prevention, or lifecycle marketing in sectors such as e-commerce, telecoms, banking, or insurance. 
• Insurance experience is not mandatory, but commercial acumen and curiosity about customer behaviour are essential. 
• Leadership experience in mentoring or managing analytics professionals.