• Project Development: Established the Data & Analytics capability at Unilever responsible for analytics project development & delivery, aiming for scalable, efficient, and cost-optimized end-to-end solutions that seamlessly integrate with existing enterprise architecture from technical, business, and user perspectives. At the later part of the role, led the MLops team with end-to-end management and deployment of multiple Machine learning projects across various functions and geographies… Show more • Project Development: Established the Data & Analytics capability at Unilever responsible for analytics project development & delivery, aiming for scalable, efficient, and cost-optimized end-to-end solutions that seamlessly integrate with existing enterprise architecture from technical, business, and user perspectives. At the later part of the role, led the MLops team with end-to-end management and deployment of multiple Machine learning projects across various functions and geographies, ensuring alignment with business objectives.• Collaboration: Worked closely with cross functional teams including data scientists, engineers and business stakeholders from sales and supply chain teams to identify data-driven opportunities and address business challenges. Developed and maintained relationships with stakeholders, providing regular updates on project status, addressing concerns, and identifying opportunities for additional collaboration. Managed the solution delivery for 60+ products collaborating with business users and engagement team across Supply Chain, Customer Development, Cross functional to deliver quality solutions• Vendor Management: Managed relationships with external vendors to leverage specialized expertise and resources for advanced analytics projects.• Performance Monitoring: Established metrics and KPIs to track the performance of analytical solutions delivery & data science initiatives and recommended adjustments to optimize outcomes. Optimized the Infrastructure cost up to 1.5 M Euros for Azure landscape by performance tuning• Continuous Improvement: Stayed updated with industry trends and emerging technologies, driving innovation and enhancing analytical capabilities. Implemented best practices for MLOps, including model versioning, continuous integration/continuous deployment (CI/CD), and model monitoring to ensure model reliability and scalability.• Team Management: Mentored team, guiding them on project execution and career development. Show less