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Data Scientist - Marketing Science

wppmedia LondonEst. Est. GBP 65,000–90,000 / yearMid

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Python
  • Sql
  • Tableau
  • Power Bi
About WPP Media WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth.  WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working. For more information, visit wppmedia.com . 3004 - Data Scientist - Marketing Science - UK Role Summary and Impact This mid-level Data Scientist role is a key role within the Marketing Science team. In this role, you will apply data-driven insights and advanced analytics to help optimise our clients' advertising campaigns and media strategies. The Marketing Science team is responsible for accelerating our clients’ growth by integrating data and analytics into marketing decision making; applying scientific rigour to marketing theory to generate data driven insight, capabilities, and consultancy. Specifically the Data Science team are a team of data practitioners who are passionate about the applications of data science within the field of marketing to make advertising better for brands and consumers; driving effective outcomes in the most sustainable way possible. They bring data to life in scientific ways to provide insight, recommendations and innovations relating to client marketing strategies from planning, audience, optimisation and measurement Your primary objective will be to leverage data analytics and machine learning techniques to drive measurable improvements in our clients' advertising campaigns. You will contribute to the development of data-driven solutions that optimize media spend, enhance audience targeting, and increase overall campaign effectiveness. Your work will directly impact our agencies ability to deliver superior results for our clients in the competitive digital advertising landscape. This role would suit a mid-level Data Scientist with around 3+ years experience, or a Junior with a couple of years relevant professional experience and ready for a step up. Responsibilities Specific responsibilities include but are not limited to: Develop and maintain media buying optimization algorithms through AI and ML to drive greater campaign outcomes against an array of KPIs Create audience segmentation and addressability models to enhance targeting precision across multiple targeting keys Generate actionable campaign insights from complex datasets to improve campaign planning through actionable insights Implement yield management strategies to maximise supply utilisation across the addressable TV portfolio Build and refine forecasting models for campaign performance for multiple channels Design and analyse A/B tests to improve campaign effectiveness measurement Assist in developing robust campaign measurement methodologies Modelling and methods Strong Python and SQL, comfortable working with large, imperfect datasets. Supervised machine learning across classification and regression, with the judgement to choose the right approach for the problem rather than defaulting to one method, including where standard linear assumptions do not hold, and quantifying uncertainty where a forecast carries commercial weight such as pricing or guarantees. Experience with experimental design, causal inference and quasi-experimental methods, including geographic experimentation (GeoLift) and test-and-control. Audience and customer segmentation using unsupervised methods, defined so segments can be activated rather than just described. Budget allocation and channel-mix work, including marketing mix modelling and the saturation and diminishing-returns dynamics behind it.

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