Junior Data Scientist

Key Purpose

• 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 on analytical, modelling, and AI initiatives under the guidance of experienced data scientists, taking increasing ownership of defined workstreams as your skills and experience develop. This role is suited to someone early in their data science career who is eager to build strong foundations in statistical thinking, machine learning, causal inference, experimentation, and AI-enabled decision systems.

You will be part of a team that:

• Develops predictive, causal, and personalised models that improve member and business outcome
• Designs experiments and measurement frameworks to determine what works, for whom, and why.
• Apply machine learning, optimisation, and AI to healthcare and operational challenges.
• Builds AI-enabled decision systems within a culture of measurement, governance, and continuous improvement.

Areas of responsibility may include but are not limited to: 

Data Analysis, Modelling and Causal Inference

• Support data analysis, feature engineering, model development, and evaluation.
• Build and refine statistical, machine learning, and causal models.
• Contribute to experimentation, impact measurement, and evaluation frameworks.
• Maintain reproducible analytical pipelines and documentation.

Experimentation and Personalisation

• Support test-and-learn initiatives from design through interpretation.
• Identify personalisation opportunities using behavioural, clinical, digital, and operational data.
• Develop models that improve targeting, prioritisation, and intervention effectiveness.

Agentic AI and AI-Enabled Workflows

• Support the development, evaluation and deployment of AI-enabled workflows that combine language models, structured data, information retrieval, and business rules.
• Assess AI solutions for reliability, safety, and business value.
• Document assumptions, limitations, risks, and failure modes.

Delivery and Communication

• Deliver well-scoped analytical, modelling, and AI-related workstreams.
• Collaborate with stakeholders to translate business problems into analytical approaches.
• Communicate findings, recommendations, and limitations clearly.

Technical Skills

Required

• 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, and model evaluation.
• Ability to write clear, reproducible, and well-documented analytical code.

Advantageous

• Exposure to causal inference, cloud platforms (preferably GCP), Git, or production data science workflows
• Exposure to generative AI, large language models, retrieval-augmented generation, or agentic AI.
• Experience applying data science in healthcare, insurance, behavioural science, or personalisation contexts.

Education and Experience

• Bachelor's or Honours degree in quantitative disciplines such as Computer Science, Data Science, Statistics, Mathematics, Actuarial Science, Operations Research, Industrial Engineering, or AppliedMathematics.
• Demonstrated aptitude for quantitative problem-solving through academic achievement, research, projects, competitions, internships, or work experience.
• Postgraduate study, research experience, or data science competition participation would be advantageous.
• Equivalent qualifications or alternative pathways will be considered where supported by strong analytical and technical capability.
• Prior industry experience is advantageous but not required.

Personal Attributes and Skills

• Curious and motivated to use data, statistics, and AI to solve meaningful healthcare and business challenges.
• Analytical, hypothesis-driven, and evidence-based.
• Strong problem-solving and communication skills.
• Comfortable with ambiguity, continuous learning, and iterative improvement.
• Able to balance multiple priorities while maintaining a broader business perspective.
• Collaborative, accountable, and proactive.
• Aligned with Discovery's values and core purpose