Sumit Waghmare

11+ years building AI and analytics systems for industrial asset operations — with deep execution experience in renewable energy infrastructure across solar, wind, and battery storage at scale. At Prescinto (acquired by IBM), I led data science for an asset performance management platform monitoring 5–10 GW of renewable assets globally — owning the analytical models behind loss attribution, equipment diagnostics, performance benchmarking, and anomaly detection. That work contributed directly to IBM's acquisition of Prescinto. Before that, at Utopus Insights (a Vestas company), I built ML systems for wind turbine predictive maintenance — including deep learning models that delivered 30+ day advance failure warning for gearbox components. Earlier, at REConnect Energy, I built generation and load forecasting pipelines integrated into grid dispatch systems. The common thread across these roles: taking raw operational telemetry from complex physical assets — inverters, turbines, BESS systems, grid interfaces — and building the analytics layer that converts it into decisions. Loss quantification, anomaly detection, root cause diagnostics, lifecycle optimization — applied to assets where underperformance has direct financial consequences. I am particularly interested in the gap between what industrial monitoring platforms promise and what operators actually experience — where diagnostics stop at dashboards, where the economics of underperformance go unmeasured, and where AI can replace manual analysis with automated operational intelligence. That problem space is where I spend most of my thinking.