Data Scientist

Requirements

• Significant experience as a Data Scientist, with a background in marketing analytics, ecommerce, or a loyalty or membership-driven environment
• Strong Python proficiency for data manipulation, statistical modelling, and machine learning, alongside advanced SQL skills and experience with dbt
• Solid grounding in statistical methods including regression, causal inference, and experimental design, with hands‑on experience building and interpreting A/B tests
• Practical experience building predictive models, particularly in retention or customer engagement contexts, with working knowledge of MLOps practices and model deployment pipelines
• Experience collaborating with CRM or lifecycle marketing teams, with the ability to shape model outputs into actionable recommendations
• The ability to communicate complex findings clearly to non‑technical stakeholders, and to build reusable data products that others can act on independently
• Familiarity with CLV modelling frameworks such as BG/NBD, Pareto/NBD, or ML‑based approaches would be a bonus, but is not essential

What the job involves

• We’re looking for a Data Scientist, Decision Sciences, with a focus on retention and member engagement
• You’ll evaluate pipelines and results of retention models and identify improvements in outputs to improve the retention strategy
• You’ll partner closely with stakeholders across the organisation, translating complex questions into well‑designed solutions, and communicating your findings in ways that land with both technical and non‑technical audiences alike
• Design, develop, and iterate on machine learning models across the member lifecycle, with a particular focus on retention, churn prediction, and customer lifetime value
• Evaluate existing model pipelines and outputs, identifying and implementing improvements to increase accuracy and business impact
• Work closely with the CRM Lifecycle team to connect model outputs to retention strategies, capturing feedback and incorporating it back into model development
• Build forecasting models and marketing mix models to support financial planning and commercial decision‑making
• Translate ambiguous business problems into well‑defined data science approaches, conducting deep‑dive analyses across member behaviour, commercial performance, and marketing effectiveness
• Build and interpret A/B tests and holdout experiments to support evidence‑based decision‑making
• Create dashboards and visualisations that help the wider organisation understand model results and progress
• Act as a trusted partner to business and analytics stakeholders, understanding their goals and proactively identifying where data science can add value

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