​​Data Science Manager

Job Title : Data Science Manager – CD XOPS Location : Bangalore About Unilever Unilever is one of the world’s leading suppliers of Food, Home, and Personal Care products, operating in over 190 countries and reaching more than 2 billion consumers every day. Our portfolio includes iconic brands such as Dove, Knorr, Domestos, Hellmann’s, Persil, Cif, Tresemmé, Rexona, and Axe. Guided by our purpose—to make sustainable living commonplace—we aim to grow our business while addressing the challenges of climate change and human development, enabling people everywhere to live well within the limits of the planet. About Global Data & Technology (GDT) Data Foundation within GDT exists to make Unilever data-intelligent, empowering critical business decisions through data, advanced analytics, and AI. Our vision is a future-fit Unilever—an organisation where data underpins decision-making across all functions: accelerating innovation, strengthening brands, driving excellence in customer execution, enhancing consumer experiences through personalisation, and transforming internal operations for efficiency and scale. 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. Key Responsibilities Technical & Product Leadership • 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. People Leadership & Capability Building • 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. Stakeholder & Business Partnership