Junior Data Scientist at Discovery Limited

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