President, Analytics

Company description

Publicis Media harnesses the power of modern media through global agency brands CJ, Performics, Publicis Collective, Publicis Health Media, Spark Foundry, Starcom and Zenith. A key business solution of Publicis Groupe (Euronext Paris FR, CAC 40), Publicis Media’s digital-first, data-driven global practices deliver client value and drive growth in a platform-powered world. It is present in more than sixty countries with over 23,000 employees worldwide.

The President, Data Science will lead Publicis Media’s audience-first data science vision and strategy, driving innovation in advanced analytics, machine learning, and AI-powered solutions. This role will oversee the development of scalable data products and predictive models that enhance media planning, audience targeting, and campaign optimization. The role requires a forward-thinking leader who can bridge business objectives with technical expertise, fostering a culture of experimentation and continuous optimization.

• Strategic Leadership:
  • Define and execute Spark Foundry’s data science roadmap aligned with agency and client growth objectives.
  • Champion the integration of AI/ML into media planning, measurement, and optimization processes.
• Innovation & Product Development:
  • Lead the creation of proprietary data science tools and platforms to deliver automation, business impact, and competitive advantage.
  • Identify emerging technologies and trends to keep Spark Foundry at the forefront of data-driven marketing.
• Client Solutions & Consulting:
  • Partner with senior client stakeholders to translate complex data insights into actionable strategies.
  • Develop advanced analytics solutions for attribution, forecasting, and ROI measurement.
• Team Leadership & Development:
  • Build and mentor an innovative data science team, fostering collaboration across analytics, engineering, and data strategy.
  • Promote diversity of thought and continuous learning within the organization.
• Collaboration:
  • Work closely with Publicis Groupe data and technology leadership to ensure alignment and scalability.
  • Collaborate with Strategy, Planning, Investment, and Analytics teams to embed data science into every stage of the agency process.

• Education: Advanced degree in Data Science, Statistics, Computer Science, or related field.
• Experience:
  • 15+ years in data science leadership roles, preferably within media, advertising, or technology sectors.
  • Proven record of building and scaling data science capabilities in a matrixed organization.
  • Experience using predictive analytics on large consumer-level data assets (e.g., Epsilon, Merkle, Acxiom) and marketer first‑party data to drive marketing outcomes.
• Technical Expertise:
  • Advanced knowledge of machine learning, AI, predictive modeling, and big data technologies.
  • Familiarity with cloud platforms (AWS, GCP, Azure) and modern data engineering practices.
  • Firsthand experience working with advanced statistical modeling and other forms of quantitative analysis, as well as knowledge of database structure and functionality.
  • Hands‑on experience with data manipulation and statistical/analytical software packages (e.g., R, MATLAB, S‑Plus, Stata, SAS, SPSS, SQL), coding/programming languages (e.g., SQL, Python, JAVA, C++), data clean room platforms (e.g., Live Ramp Safe Haven, Epsilon People Cloud, Amazon Marketing Cloud).
• Leadership
  
  Skills:
  
  
  • Exceptional ability to influence senior stakeholders and communicate complex concepts clearly.
  • Strong business acumen with a passion for innovation and measurable impact.

• Data Science Adoption:
  • Percentage of media planning and optimization processes leveraging AI/ML models.
• Innovation Impact:
  • Number of proprietary data science tools launched and adopted by client teams.
• Client Value Creation:
  • Improvement in campaign ROI and attribution accuracy driven by data science solutions.
• Revenue Contribution:
  • Incremental revenue generated from data science‑driven products and services.
• Operational Efficiency:
  • Reduction in time‑to‑insight and automation of manual analytics processes.
• Talent Development:
  • Retention rate and growth of data science team; number of advanced certifications…