About Discovery Discovery's core purpose is to enhance and protect people's lives through products and services that use incentives, behavioural science and data to encourage healthier choices. With extensive data assets across healthcare, wellness, lifestyle, driving, investments and life insurance, Discovery provides data scientists with opportunities to apply advanced analytics, machine learning, causal inference and artificial intelligence to real-world challenges. About the Data Science Lab The Data Science Lab (DS Lab) is a specialist team that works on high-impact problems across Discovery Health and the broader Discovery Group. The team uses large-scale structured and unstructured data to develop predictive models, experiments, decision systems and AI-enabled solutions that create measurable value for members and the business. The DS Lab works with teams across Discovery and external organisations, including technology companies and leading academic institutions, to advance areas such as personalisation, causal inference, optimisation and artificial intelligence. As a Junior Data Scientist, you will have the opportunity to work alongside experienced data scientists, gain exposure to advanced technologies and contribute to solutions that have a real-world impact. Key Purpose of the Role The purpose of this role is to support the delivery of data science, machine learning, causal inference and AI solutions that improve member engagement, health outcomes, operational performance and business decision-making. You will work under the guidance of experienced data scientists while gradually taking greater ownership of defined projects and workstreams. This opportunity is suited to someone at an early stage of their data science career who wants to develop strong foundations in: Statistical analysis. Machine learning. Causal inference. Experimentation. Artificial intelligence. AI-enabled decision systems. Key Areas of Responsibility Data Analysis, Modelling and Causal Inference Support data analysis and feature engineering. Assist with machine learning model development and evaluation. Build and refine statistical, machine learning and causal models. Contribute to experimentation and impact measurement initiatives. Support evaluation frameworks to assess the effectiveness of interventions. Maintain reproducible analytical pipelines and clear documentation. Experimentation and Personalisation Support test-and-learn initiatives from design through to interpretation. Identify opportunities for personalisation using behavioural, clinical, digital and operational data. Develop models that improve targeting, prioritisation and intervention effectiveness. AI and AI-Enabled Workflows Support the development, evaluation and deployment of AI-enabled workflows. Work with language models, structured data, information retrieval and business rules. Assess AI solutions for reliability, safety and business value. Document assumptions, limitations, risks and potential failure modes. Delivery and Communication Deliver well-defined analytical, modelling and AI-related workstreams. Work with stakeholders to understand business challenges and translate them into analytical solutions. Clearly communicate findings, recommendations and limitations. Collaborate with team members and stakeholders across the business. Required Technical Skills Applicants should have: Proficiency in Python for data analysis, statistical modelling and machine learning. Experience working with SQL, relational databases and structured data. Strong foundations in: Statistics Machine learning Experimental design Model evaluation Ability to write clear, reproducible and well-documented analytical code. Advantageous Skills and Experience The following will be beneficial: Exposure to causal inference. Experience with cloud platforms, preferably Google Cloud Platform (GCP). Experience using Git. Exposure to production data science workflows. Knowledge of generative AI and large language models. Exposure to retrieval-augmented generation (RAG). Exposure to agentic AI. Experience applying data science in healthcare, insurance, behavioural science or personalisation. Education and Experience Applicants should have a: Bachelor's or Honours degree in a quantitative discipline such as: Computer Science Data Science Statistics Mathematics Actuarial Science Operations Research Industrial Engineering Applied Mathematics Candidates should demonstrate an aptitude for quantitative problem-solving through academic achievement, research, projects, competitions, internships or relevant work experience. The following will be advantageous: Postgraduate studies. Research experience. Data science competition participation. Previous industry experience. Prior industry experience is advantageous but not required. Equivalent qualifications or alternative career pathways may also be considered where candidates can demonstrate strong analytical and technical capabilities. Personal Attributes and Skills The ideal candidate should be: Curious and motivated to use data, statistics and AI to solve meaningful healthcare and business challenges. Analytical and evidence-based in their approach to problem-solving. Hypothesis-driven and comfortable working with data. A strong problem solver and communicator. Comfortable working with ambiguity. Willing to continuously learn and improve. Able to manage multiple priorities while maintaining a broader business perspective. Collaborative, accountable and proactive. Aligned with Discovery's values and purpose. Employment Equity Discovery's approved Employment Equity Plan and targets will be considered as part of the recruitment process. As an equal opportunities employer, Discovery actively encourages and welcomes people with disabilities to apply. Why Apply? This is an exciting opportunity for an early-career data scientist to work with Python, SQL, machine learning, causal inference and AI while contributing to innovative solutions across healthcare, insurance and financial services. The role offers exposure to experienced data scientists, advanced technologies and projects designed to create measurable real-world impact.