Employer: Discovery Health
Business Unit: Data Sciences (Data Science Lab / DS Lab)
Industry: Healthcare Analytics, Health Insurance, Behavioral Economics & Applied AI
Location: Discovery Place, Sandton, Gauteng, South Africa (On-Site)
Position Type: Full-Time Permanent
Target Candidates: Quantitative Graduates & Early-Career Data Scientists (BSc / Honours in CS, Data Science, Stats, Maths, Actuarial, or Engineering)
Experience Level: Entry-Level to Junior (Prior industry experience is advantageous but NOT required!)
Compensation: Market-Related Financial Services & Health Tech Salary
Posted Date: 27 October 2026
Closing Date: 27 November 2026
About Discovery Health & The Data Science Lab (DS Lab)
Discovery Health leverages incentives, behavioral science, and world-class data assets to enhance and protect people’s lives, creating shared value across healthcare ecosystems globally.
Discovery’s prestigious Data Science Lab (DS Lab) is a specialist analytical team that solves high-impact problems across Discovery Health, Vitality, and international partner markets. Partnering with global leaders such as Google and Quantium Health, the DS Lab builds cutting-edge causal inference models, machine learning algorithms, Personal Health Pathways (PHP), and Agentic AI-enabled decision systems.
Discovery Health is recruiting a passionate Junior Data Scientist to join the DS Lab in Sandton, Gauteng. Working under senior data science mentorship, the successful candidate will build predictive models, design experimental measurement frameworks, run test-and-learn personalization initiatives, and evaluate Agentic AI workflows combining Large Language Models (LLMs) with structured healthcare data.
Key Responsibilities & Data Science Workflows
The Junior Data Scientist will execute four core technical and analytical deliverables:
1. Data Analysis, Modelling & Causal Inference
- Feature Engineering & Modelling: Extract, clean, and engineer features using SQL and Python to build and refine predictive statistical, machine learning, and causal inference models.
- Reproducible Pipelines: Maintain clear, well-documented, reproducible data science pipelines and version-controlled repositories.
- Causal Measurement: Contribute to causal inference frameworks, impact measurement, and experimental evaluation to determine true program efficacy.
2. Experimentation & Personalization
- Test & Learn Frameworks: Support A/B testing and experimentation initiatives from experimental design through statistical interpretation.
- Targeting Models: Develop models utilizing clinical, behavioral, digital, and operational data to personalize member health interventions and improve targeting accuracy.
3. Agentic AI & Generative AI Workflows
- AI System Evaluation: Support the development, benchmarking, and deployment of AI-enabled workflows combining LLMs, structured databases, RAG (retrieval-augmented generation), and business logic.
- Safety & Governance: Assess AI solutions for reliability, safety, hallucination risks, and business value while documenting failure modes.
4. Stakeholder Delivery & Communication
- Problem Translation: Translate complex clinical and business problems into rigorous analytical methodologies.
- Technical Presentation: Communicate mathematical findings, model limitations, and strategic recommendations clearly to non-technical stakeholders.
Candidate Profile & Technical Entry Requirements
To qualify for consideration, applicants must meet the following mandatory academic and technical criteria:
- Educational Qualifications (MANDATORY DEGREE):
- Completed Bachelor’s Degree or Honours Degree in a quantitative discipline such as Data Science, Computer Science, Statistics, Mathematics, Actuarial Science, Operations Research, Industrial Engineering, or Applied Mathematics (Mandatory).
- Demonstrated quantitative problem-solving through academic excellence, research papers, capstone projects, hackathons, Kaggle competitions, or internships.
- Core Technical Stack (MANDATORY SKILLS):
- Proficiency in Python for data manipulation, statistical modeling, and machine learning (Mandatory).
- Proficiency in SQL for querying relational databases and processing structured data (Mandatory).
- Strong foundations in statistical theory, machine learning concepts, experimental design, and model evaluation metrics (Mandatory).
- Advantageous Technical Skills:
- Exposure to Causal Inference, Google Cloud Platform (GCP), Git, or production data science CI/CD workflows.
- Exposure to Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or Agentic AI.
- Experience applying data science within healthcare, insurance, behavioral science, or personalization domains.
- Work Experience Level:
- Prior industry experience is NOT strictly required (Ideal for exceptional quantitative graduates or candidates early in their data science career).
Application Guidelines & Online Submission
- Prepare Document Package:
- Detailed Curriculum Vitae (CV) highlighting your Python, SQL, statistical modeling projects, and GitHub or Kaggle profiles.
- Certified Copy of South African ID Document or Passport.
- Certified Copies of Tertiary Degree Certificates & Official Academic Transcripts.
- Online Portal: Submit your application directly via the official Discovery Careers Portal searching Junior Data Scientist – Discovery Health.
- Closing Date: Applications are processed on a rolling basis; early submission is strongly recommended.
Sample Selection & Data Science Interview Questions
Machine Learning, SQL & Causal Inference
- Question (SQL & Feature Engineering): “How do you write an efficient SQL query using window functions to calculate rolling 30-day clinical claim counts for a member cohort in a multi-million row database?”
- Question (Causal Inference vs Predictive ML): “Why is standard predictive machine learning insufficient when trying to measure the true impact of a health intervention program like Personal Health Pathways, and how does causal inference solve this?”
How to Apply
Apply online directly via the official Discovery Careers Portal:
- Employer: Discovery Health
- Division: Data Science Lab (DS Lab)
- Location: Sandton, Gauteng
- Position: Junior Data Scientist
- Education: BSc / Honours in Data Science, CS, Stats, Maths, Actuarial, or Engineering
- Experience: Entry-Level / Junior (No prior experience required)