Data Scientist - Consumer Technology Platform

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Harnham
ScreenedHybrid
London, City of London
Posted 2 weeks ago
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About the role

wn machine learning that drives decisions across a 4M+ member platform

Build commercial models spanning retention, recommendations and customer growth

Work directly with senior stakeholders in a growing Data Science team



Like the look of this opportunity Make sure to apply fast, as a high volume of applications is expected Scroll down to read the complete job description.

Up to £90,000 + 7% Bonus

Hybrid | London (2 days/week) - Leicester office visit once a month included


Join one of the UK's largest membership platforms, using machine learning to improve engagement, retention and commercial performance for over 4 million members.

Working within a small, high-impact Data Science team, you'll develop models across retention, CLV, recommendations, search, forecasting and anomaly detection, partnering closely with Product, Marketing, CRM and Finance.


The Role

  • Build retention, churn and CLV models
  • Develop recommendation and search algorithms
  • Create forecasting and marketing mix models
  • Detect fraud, broken journeys and offer issues
  • Design and evaluate A/B tests
  • Translate business problems into scalable ML solutions
  • Tech stack: Python, SQL, Databricks, dbt, MLflow


Requirements

  • 5+ years' commercial Data Science experience
  • Strong Python and SQL skills
  • Experience with customer, marketing, ecommerce or loyalty data
  • Background in retention, CLV, recommendations or experimentation
  • Excellent stakeholder management
  • Databricks, dbt or MLflow exposure is beneficial


Why Join? xwzovoh

  • Up to £90,000 + 7% bonus
  • Hybrid working (2 days/week)
  • High commercial ownership and visibility
  • Dedicated MLOps support
  • Opportunity to shape strategy through machine learning


If you're looking for a role where your models directly influence customer experience, commercial performance and strategic decision making, please apply below.

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