Summary
Sijo Manikandan is a data science leader with nine years of experience building AI-driven forecasting, anomaly detection, and optimization systems that directly influence retail and operational outcomes. As Director of Data Science at SKIMS and former Lead Data Scientist at Nike, he has delivered large-scale demand and turnover forecasting models that improved inventory, staffing, and expansion planning across hundreds of locations. His background includes enterprise-scale applied-AI work at Quantiphi—creating 15-minute interval forecasts for 2,500+ stores—and consulting roles that standardized metrics and automated workflows for major healthcare, automotive, and technology clients. Sijo blends product-focused model development with strong stakeholder partnership, turning complex forecasting problems into operational tools that protect and grow revenue. He holds a Master’s in Business Analytics from Texas McCombs and brings a pattern of shipping MVPs that accelerate decision-making, not just research prototypes. A less obvious strength is his track record of translating probabilistic forecasts into actionable targets for new-store openings and franchise decisions, bridging analytics with real-world business strategy.
9 years of coding experience
8 years of employment as a software developer
Master's degree Business Analytics, Master's degree Business Analytics at Texas McCombs School of Business
BITS Pilani, Birla Institute of Technology and Science
English, Malayalam, Hindi, Tamil