Discovery Health 

Data Scientist 

About Discovery 

Discovery’s core purpose is to enhance and protect people’s lives. It does this through breakthrough product designs that harness incentives to encourage people to make healthier lifestyle choices. Healthy behaviour leads to lower claims, higher margins, and lower lapses. These savings are shared with our clients, which in turn leads to a healthier society, improved productivity, and a reduced healthcare burden. One of Discovery’s core assets is its large and diverse data, covering health, wellness, driving, investments, and life insurance. This forms the basis for our shared value model, along with innovation, risk management, and operational efficiency improvements. Discovery’s energetic and motivated analytical teams make this happen. 

About the Data Science Lab

The Group Data Science Lab (DS Lab) is expanding, and we’re looking for talented individuals to join our growing team. Our global team collaborates with partners such as Discovery Health Digital, Quantium, Risk Intelligence, and renowned institutions like the London School of Economics (LSE). Working across diverse areas—including digital, clinical, wellness, and behavioral themes—we harness terabytes of structured and unstructured data using a modern big data architecture. The DS Lab also embraces a forward-thinking approach, identifying new data sources and opportunities and leveraging the Vit.AI platform, we create scalable, fit-for-purpose solutions that serve the business well into the future.  

Key Purpose

Your primary purpose will be to support and contribute to the delivery of data science, machine learning, and AI use cases that improve member engagement, operational efficiency, and product outcomes. You will take ownership of defined analytical and modelling tasks and deliver well‑scoped use cases or components of larger projects with increasing autonomy. You will work closely with senior data scientists on more complex initiatives, while remaining accountable for the quality and timeliness of your own delivery.

You will be part of a team that:

• Builds personalised and predictive models that encourage healthy behaviours and communication while improving member outcomes
• Uses experimentation, A/B testing, and analytics to inform decision-making
• Applies machine learning and emerging AI techniques, including Large Language Models, optimisation models and agents, to real business problems
• Operates in a test learn adapt culture where learning and iteration are core to delivery

Areas of responsibility may include but not limited to

• Data Analysis and Modelling
  • Support senior data scientists in exploratory data analysis, feature engineering, and model development
  • Build, evaluate, and refine statistical and machine learning models under guidance
  • Develop and maintain components of data science pipelines, including data preparation, model training, and performance monitoring
  • Contribute to experimentation frameworks such as A/B tests and uplift modelling
  • Ensure analytical work is reproducible, well-documented, and aligned with production standards
• Delivery Ownership
  • Take ownership of clearly defined tasks or sub projects within larger initiatives
  • Independently deliver smaller, well‑scoped analyses or modelling use cases with supervision
  • Manage own deliverables against agreed timelines, escalating risks or blockers early
  • Support model validation, testing, and post‑deployment analysis
• Collaboration and Stakeholder Engagement
  • Work closely with senior data scientists to translate business questions into analytical problems
  • Collaborate with engineering and systems teams to support model deployment into production environments
  • Engage with product and business stakeholders to understand objectives, constraints, and success measures
  • Present findings, insights, and model outputs in a clear and structured way for technical and non technical audiences

Personal Attributes and Skills 

• A creative and enthusiastic attitude to unearthing valuable insights by solving real-world problems using data and generating value for Discovery clients 

• Ability to balance multiple priorities and to step back and see how analytics work fits into the wider business context 
• Ability to formulate a clear problem statement, develop a plan for tackling it, and clearly communicate findings verbally, visually, and in writing
• Results driven, curious and able to work autonomously or within teams
• Strong communication skills for both technical peers and business partners
• Comfortable working with guidance, while proactively taking responsibility for deliverables
• Aligned to Discovery values and core purpose 

Technical Skills

Required

• Proficiency in Python for data analysis and applied machine learning
• Experience working with SQL and relational databases
• Solid understanding of data analysis, statistics and core machine learning concepts
• Ability to work with structured and semi‑structured data

Advantageous

• Exposure to cloud platforms (GCP preferred; AWS or Azure acceptable)
• Experience using Git or other version control systems

Education and Experience

• Bachelor or Honours in Computer Science, Mathematics, Statistics, Data Science, Actuarial Science, Statistics, Operations Research, Industrial engineering, Applied Mathematics, or similar quantitative field.
• Master’s or PhD degree in the above fields would be advantageous.
• Other qualifications will also be considered if accompanied by relevant experience.
• Demonstrable and relevant working experience in an analytics position, where the focus was on building and implementing machine learning models to solve business problems

EMPLOYMENT EQUITY   
                             
The Company’s approved Employment Equity Plan and Targets will be considered as part of the recruitment process. As an Equal Opportunities employer, we actively encourage and welcome people with various disabilities to apply.