Lead Data Scientist (Retail & Wholesale, AI Initiatives), Lotus's

ResponsibilitiesLeadership and Strategy • Drive the end-to-end AI and Advanced Analytics initiatives to support CP Axtra’s Retail & Wholesale businesses. • Develop and execute a forward-looking AI strategy that delivers measurable impact on revenue growth, cost efficiency, and customer engagement. • Serve as a bridge between business and technology, ensuring AI adoption and scaling across multiple business units. • Stay updated on global and local AI trends, including Generative AI, personalization, forecasting, and optimization, to strengthen CP Axtra’s competitive edge.

Analytics and AI Execution • Lead the design, development, and deployment of machine learning and AI models, including personalization engines, pricing optimization, demand forecasting, inventory management, and GenAI/NLP applications. • Oversee experimentation, validation, and monitoring of AI/ML models to ensure scalability, reliability, and business integration. • Ensure close collaboration with data engineering teams to enable robust pipelines and MLOps for production-grade solutions.

Business Partnership • Partner with Retail Operations, Marketing, Sales, Supply Chain, Finance, and IT to co-create AI use cases and drive adoption. • Build strong relationships with stakeholders to align priorities, communicate trade-offs, and manage expectations effectively. • Act as a trusted advisor to senior executives, translating complex AI insights into actionable recommendations. • Evangelize the value of AI and data-driven decision-making across the organization.

People Leadership and Collaboration • Mentor and coach a team of data scientists and analysts, fostering a culture of innovation, experimentation, and continuous learning. • Promote cross-functional collaboration with Business Intelligence and Data Engineering teams to deliver integrated solutions. • Encourage knowledge sharing and build internal AI/ML capability to strengthen organizational maturity.

Performance Monitoring and Optimization • Define and monitor success metrics for AI initiatives, such as sales uplift, campaign ROI, operational cost reduction, and customer lifetime value. • Continuously assess and optimize AI-driven processes to maximize business impact. • Share learnings, case studies, and success stories to build trust and ensure alignment with business leaders.