Key Purpose<\/p>\n
\tYour 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\u2011scoped 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.<\/p>\n
You will be part of a team that:<\/p>\n
\tBuilds personalised and predictive models that encourage healthy behaviours and communication while improving member outcomes
\n\tUses experimentation, A\/B testing, and analytics to inform decision-making
\n\tApplies machine learning and emerging AI techniques, including Large Language Models, optimisation models and agents, to real business problems
\n\tOperates in a test learn adapt culture where learning and iteration are core to delivery<\/p>\n
Areas of responsibility may include but not limited to
\nData Analysis and Modelling<\/p>\n
\tSupport senior data scientists in exploratory data analysis, feature engineering, and model development
\n\tBuild, evaluate, and refine statistical and machine learning models under guidance
\n\tDevelop and maintain components of data science pipelines, including data preparation, model training, and performance monitoring
\n\tContribute to experimentation frameworks such as A\/B tests and uplift modelling
\n\tEnsure analytical work is reproducible, well-documented, and aligned with production standards<\/p>\n
Delivery Ownership<\/p>\n
\tTake ownership of clearly defined tasks or sub projects within larger initiatives
\n\tIndependently deliver smaller, well\u2011scoped analyses or modelling use cases with supervision
\n\tManage own deliverables against agreed timelines, escalating risks or blockers early
\n\tSupport model validation, testing, and post\u2011deployment analysis<\/p>\n
Collaboration and Stakeholder Engagement<\/p>\n
\tWork closely with senior data scientists to translate business questions into analytical problems
\n\tCollaborate with engineering and systems teams to support model deployment into production environments
\n\tEngage with product and business stakeholders to understand objectives, constraints, and success measures
\n\tPresent findings, insights, and model outputs in a clear and structured way for technical and non technical audiences<\/p>\n
Personal Attributes and Skills\u00a0<\/p>\n
\tA creative and enthusiastic attitude to unearthing valuable insights by solving real-world problems using data and generating value for Discovery clients\u00a0
\n\tAbility to balance multiple priorities and to step back and see how analytics work fits into the wider business context\u00a0
\n\tAbility to formulate a clear problem statement, develop a plan for tackling it, and clearly communicate findings verbally, visually, and in writing
\n\tResults driven, curious and able to work autonomously or within teams
\n\tStrong communication skills for both technical peers and business partners
\n\tComfortable working with guidance, while proactively taking responsibility for deliverables
\n\tAligned to Discovery values and core purpose\u00a0<\/p>\n
\u00a0Technical Skills
\nRequired<\/p>\n
\tProficiency in Python for data analysis and applied machine learning
\n\tExperience working with SQL and relational databases
\n\tSolid understanding of data analysis, statistics and core machine learning concepts
\n\tAbility to work with structured and semi\u2011structured data<\/p>\n
Advantageous<\/p>\n
\tExposure to cloud platforms (GCP preferred; AWS or Azure acceptable)
\n\tExperience using Git or other version control systems<\/p>\n
Education and Experience<\/p>\n
\tBachelor or Honours in Computer Science, Mathematics, Statistics, Data Science, Actuarial Science, Statistics, Operations Research, Industrial engineering, Applied Mathematics, or similar quantitative field.
\n\tMaster\u2019s or PhD degree in the above fields would be advantageous.
\n\tOther qualifications will also be considered if accompanied by relevant experience.
\n\tDemonstrable and relevant working experience in an analytics position, where the focus was on building and implementing machine learning models to solve business problems<\/p>\n
go to method of application \u00bb<\/p>\n
Apply via company website ( ) or<\/p>\n
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