Solution, Services & Product Development

Global Head of Measurement and Optimization, Data & Tech Solution, Services & Product Development

The Global Head of Measurement and Optimization, Data & Tech is responsible for shaping and executing the strategic vision for dentsu’s analytics product suite. This role leads the development and delivery of enterprise analytics capabilities that drive business transformation, sitting at the intersection of data strategy, artificial intelligence, and product management.

At dentsu, analytics sits at the heart of everything we do—transforming data into actionable insights and predictive intelligence that fuel smarter decisions, optimized investments, and measurable impact. It’s not just a step in the process; it’s the engine that powers innovation and growth across every stage of our client journey.

The incumbent will own a portfolio of analytics products, define detailed product strategies and features, prioritize initiatives based on business value, and ensure successful execution across global teams.

Partnering closely with dentsu leadership and the global Solutions group, this role ensures alignment of product roadmaps across dentsu.connect and consistency in strategy execution. Success is measured by the creation and delivery of scalable, intelligent, and value-driven analytics solutions that leverage automation, insights, and advanced analytics to achieve organizational goals.

The ideal candidate brings deep business acumen, technical fluency in data and AI, and a proven ability to guide cross-functional teams in delivering impactful analytics products that support global business objectives.

Key Responsibilities

Strategic Leadership

• Define and execute the enterprise analytics product strategy aligned with organizational objectives.
• Ensuring analytics and AI capabilities are reliable, reusable, and measurable.
• Influence long-term investments in data platforms, machine learning infrastructure, and AI governance.
• Collaborate with data science and engineering teams to operationalize machine learning models, generative AI, and predictive analytics use cases.
• Shape and manage a portfolio of analytics products, from experimentation to enterprise-scale deployment.
• Ensure explainability, ethical AI use, and model performance monitoring in partnership with governance and risk teams.

Product Ownership & Delivery

• Own the end-to-end lifecycle of analytics products—from ideation through delivery and adoption.
• Maintain and prioritize the strategic backlog, balancing innovation, data quality, and user impact.
• Translate complex analytical capabilities into actionable tools and experiences for business users.
• Partner with engineering to ensure scalable, secure, and well-architected solutions.

Stakeholder Engagement & Change Leadership

• Serve as a trusted advisor to senior business leaders, shaping how analytics inform strategy and operations.
• Evangelize data-driven and AI-enabled decision-making across the organization.

Team Leadership:

• Manage a team of globally distributed employees
• Provide dynamic and visible leadership to the Analytics Product team, fostering a culture of innovation, collaboration, and continuous improvement
• Develop and mentor junior team members, building a modern, agile workforce capable of adapting to and leading in a rapidly changing technological landscape
• Set clear objectives and performance expectations, providing regular feedback and recognition to drive team success

Required Qualifications

• Bachelor’s degree in Business, Computer Science, Data Science, or related field;
  Master’s or advanced degree in analytics, AI, or technology management preferred.
• 8+ years of experience in data product management, analytics, or strategy, including at least 5+ years in a senior or leadership role, with solid project management experience across the full product lifecycle (strategy, roadmap, delivery, optimization).
• Proven experience leading large-scale analytics or AI initiatives, translating complex business needs into technical requirements and actionable user stories.
• Strong understanding of modern data ecosystems (e.g., Snowflake, Databricks, Azure, AWS) and analytics platforms (e.g., Power BI, Tableau, Looker), with…