Data Science Manager

About the role

As a Data Science Manager within the Customer Development XOPS team in Data Foundation, you will be responsible for leading data science delivery across products and markets, shaping technical direction, and developing high-performing teams. This is a hands-on leadership role, combining technical depth with people management and strong business partnership. You will own the end-to-end data science lifecycle for key initiatives—translating business problems into scalable analytics solutions, ensuring high-quality delivery, and embedding data science into decision-making at scale.

What we offer: a leadership role shaping high-impact, global data science products; the opportunity to build and lead teams working on cutting-edge analytics, AI, and cloud technologies; a collaborative, inclusive culture that values ownership, learning, and innovation; direct contribution to Unilever’s growth ambitions and sustainability purpose.

Responsibilities

• Own and shape the data science roadmap for products within our Customer Development (CD) portfolio, aligned to commercial priorities and business outcomes.
• Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions, ensuring robustness, scalability, and interpretability.
• Set modelling and analytical standards across the team, covering classical statistical methods, machine learning, and emerging Generative and Agentic AI capabilities.
• Ensure solutions are industrialised and production-ready, leveraging cloud platforms (Azure, Databricks) and aligned with MLOps best practices.
• Lead, coach, and develop a team of data scientists, supporting both technical growth and career progression.
• Foster a culture of high ownership, continuous improvement, and learning, encouraging experimentation while maintaining delivery discipline.
• Provide technical guidance and review, acting as a mentor and escalation point for complex analytical challenges.
• Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science solutions.
• Translate complex analytics into clear, actionable insights for senior stakeholders, supporting data-driven decision-making.
• Balance short-term delivery with long-term capability building, ensuring alignment between local market needs and global product strategy.
• Champion agile ways of working, enabling fast-paced experimentation and iterative delivery.