SVP, Data Science Lead, CoE - Global Clients

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.

• This role is for Data Science across Global Clients. Provide expert oversight across statistical modelling, econometrics, machine learning, AI, forecasting, optimisation, attribution, propensity modelling and experimentation, ensuring methodologies are robust, explainable, reproducible and appropriate to the business problem.
• Team Leadership, Technical Mentoring & Capability Building: Lead, mentor and develop the Data Science team, with particular emphasis on technical coaching and craft excellence. Create an environment in which data scientists can challenge methods, learn from each other, deepen their technical expertise and progress their careers. Establish and champion best practices for code, model validation, documentation, QA, version control and model governance.
• Data Science Product & Service Portfolio Leadership: Own the technical stewardship and ongoing evolution of the Data Science CoE/Hub portfolio within the Analytics & Data Science Menu of Services. This includes Propensity Modelling, ML driven econometrics, MTA approaches, and other advanced measurement, attribution, forecasting & simulation solutions. Ensure solutions remain differentiated, scalable, operationally sound and connected to client needs.
• Innovation & Solution Development: Partner with the Head of CoE, DTA leaders and adjacent product, data, technology and consulting teams to identify high-value opportunities for new Data Science solutions. Translate emerging techniques in AI, machine learning, econometrics and optimisation into practical, repeatable offerings that can be piloted, proven and scaled across clients.
• Technical Assurance & Delivery Excellence: Provide senior technical review and challenge on complex projects, from problem framing and data requirements through modelling, validation, deployment and interpretation. Establish consistent development and QA standards so that solutions are accurate, transparent, maintainable and fit for scaled client delivery.
• Cross-Functional DTA Partnership: Act as the senior Data Science partner across the wider DTA organisation, helping Data Strategy, Client Analytics, DTA Leads, Client Enablement and technology partners understand where Data Science can create differentiated value. Bring technical expertise into complex client opportunities and connect specialist teams around the right solution.
• Client & Commercial Leadership: Serve as a senior technical advisor on priority client engagements and pitches, translating complex modelling and AI approaches into clear business value. Support solution scoping, resourcing and commercialisation of Data Science projects and managed services, balancing technical ambition with delivery feasibility and client impact.
• Thought Leadership & Community: Build a strong Data Science craft community across the CoE, Global Client Teams & partner global hubs, sharing reusable methods, code, learning and case studies. Stay current with developments in statistical science, machine learning, AI, marketing effectiveness and media technology, and turn relevant advances into practical improvements to our people, products and client solutions.

• Senior Data Science Leadership: Significant experience leading Data Science teams, with a track record of building strong technical capability, developing senior talent and setting a clear technical vision in a complex, matrixed organisation. Experience in the media advanced analytics space desirable.
• Deep Technical Expertise: Expert-level understanding of statistical modelling, machine learning, AI and predictive analytics, with the depth to review, challenge and mentor technical work rather than operating solely as a people or commercial leader.
• Modelling & Measurement Breadth: Strong experience across a range of methods relevant to marketing and media, such as econometrics/MMM, attribution, propensity and classification models, forecasting, optimisation, experimentation, model validation and scenario planning.
• Programming & Data Fluency: Strong proficiency in Python, R and/or SQL, with a practical understanding of reproducible analytical workflows, data pipelines, APIs, cloud data environments, version control, model QA and deployment.
• Product & Solution Leadership: Demonstrated ability to turn technical methodologies into repeatable products or services, manage an existing solution portfolio, establish technical standards and prioritise a roadmap of enhancements and new capabilities.
• Innovation Mindset: Evidence of identifying and applying new techniques to real business problems, moving from experimentation to robust, scalable solutions while maintaining appropriate governance, transparency and responsible AI practices.
• Media & Marketing Acumen: Strong understanding of how data science supports media planning, buying, measurement and optimisation, and how technical outputs translate into client business outcomes and investment decisions.
• Stakeholder & Communication Skills: Ability to act as a trusted technical partner to senior business, client and DTA stakeholders, translating complex concepts into clear choices, implications and commercial value.
• Delivery & Commercial Acumen: Experience overseeing a portfolio of complex analytical projects, including scoping, resourcing, quality, risk and stakeholder expectations, with the judgement to balance innovation, scalability and commercial viability.