Data Scientist (Decision Sciences)

TL;DR

Data Scientist (Decision Sciences): Developing and iterating on machine learning models to understand member behavior with an accent on retention, churn prediction, and customer lifetime value. Focus on evaluating model pipelines, building forecasting models, and conducting A/B tests to drive evidence-based commercial decisions.

Location: Hybrid (HQ in Leicestershire or London, Holborn office); fully remote working is not available

Company

A loyalty platform providing exclusive discounts and services to emergency services, NHS, and social care workers.

What you will do

• Design, develop, and iterate on ML models across the member lifecycle, focusing on retention and CLV.
• Evaluate and improve existing model pipelines to increase accuracy and business impact.
• Collaborate with the CRM Lifecycle team to translate model outputs into actionable retention strategies.
• Build forecasting and marketing mix models to support financial planning and commercial decisions.
• Design and interpret A/B tests and holdout experiments for evidence-based decision-making.
• Create dashboards and visualizations to communicate model results to the wider organization.

Requirements

• Significant experience as a Data Scientist in marketing analytics, ecommerce, or loyalty environments.
• Strong proficiency in Python and advanced SQL, including experience with dbt.
• Solid grounding in statistical methods, causal inference, and experimental design.
• Practical experience building predictive models for retention or customer engagement.
• Working knowledge of MLOps practices and model deployment pipelines.
• Ability to communicate complex findings clearly to non-technical stakeholders.

Nice to have

• Familiarity with CLV modelling frameworks such as BG/NBD or Pareto/NBD.

Culture & Benefits

• Hybrid working and flexible hours.
• Generous funded BUPA medical insurance covering pre-existing conditions.
• Company bonus scheme and personal growth fund.
• 25 days annual leave plus a birthday day off and a holiday buy/sell scheme.
• Onsite gym and EV charging at HQ, along with a relaxed dress code.
• Strong learning and development culture with regular social events.