Growth Lifecycle Optimization Lead (Remote UK)

Notable Capital

United Kingdom

Teletrabalho

GBP 90 000 - 130 000

Tempo integral

14 dias+

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Resumo da oferta

Lime is seeking a Growth Optimization - Lifecycle Marketing Manager to own and scale our rider and user retention ecosystem from UK remote location. You’ll blend marketing analytics, data science, and UX design to build end-to-end lifecycle campaigns in Braze and beyond.

You’ll design advanced experiments, optimize offer allocations, and work with Data Science on segmentation models to drive higher LTV and activation while coordinating cross-functional teams in Jira.

Qualificações

  • 4+ years of lifecycle or growth marketing in a fast-paced B2C environment.
  • Strong data science concepts and experience with experiments (A/B or MAB).
  • Proficiency with Braze and API triggers, Liquid personalization, Canvas.

Responsabilidades

  • End-to-End Execution: own lifecycle campaigns and cross-channel strategy.
  • Advanced Experimentation Design: design and measure multi-armed bandit experiments.
  • Algorithmic Segmentation: partner with Data Science on clustering and churn models.
  • Lifecycle System Architecture: design and iterate on onboarding, activation, churn prevention.
  • CRM as a Product: treat CRM as a scalable product with attributes and events.
  • Project & Execution Management: manage Jira workflows and asset pipelines.

Conhecimentos

Lifecycle marketing
Data literacy
Braze
SQL
Tableau/Looker
JIRA
Python

Formação académica

Bachelor's degree in Marketing, Operations Research, Business, Economics or related field

Ferramentas

Braze

Descrição da oferta de emprego

Lime is seeking a Growth Optimization - Lifecycle Marketing Manager to own and scale our rider and user retention ecosystem from UK remote location. You’ll blend marketing analytics, data science, and UX design to build end-to-end lifecycle campaigns in Braze and beyond.

You’ll design advanced experiments, optimize offer allocations, and work with Data Science on segmentation models to drive higher LTV and activation while coordinating cross-functional teams in Jira.

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