Sumit Waghmare

AI Researcher

Bengaluru, Karnataka, India10 yrs 8 mos experience
Highly Stable

Key Highlights

  • Led data science for IBM's renewable asset platform.
  • Developed predictive maintenance models for wind turbines.
  • Achieved high accuracy in electricity load forecasting.
Stackforce AI infers this person is a Renewable Energy Data Scientist with expertise in machine learning and analytics.

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Skills

Core Skills

Data ScienceAnalyticsMachine LearningPredictive MaintenanceForecasting

Other Skills

Data ArchitectureTeam ManagementDeep LearningAnomaly DetectionResearchData AnalysisMatlabRProgrammingMicrosoft OfficeMicrosoft ExcelData AnalyticsPowerPointMicrosoft WordSmart Grid

About

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.

Experience

10 yrs 8 mos
Total Experience
2 yrs 4 mos
Average Tenure
1 yr 2 mos
Current Experience

Ibm

Advisory Data Scientist

Apr 2025Present · 1 yr 2 mos · Greater Bengaluru Area · Hybrid

  • Continuing advanced analytics work within the renewable energy asset performance domain following Prescinto's acquisition by IBM.

Prescinto, an ibm company

Data Science Manager

May 2021Mar 2025 · 3 yrs 10 mos · Bengaluru

  • Identified the product gap in renewable APM analytics, defined the capability roadmap, and built the Wind and BESS analytics suite from zero — power curve benchmarking, automatic cycle detection, string/module imbalance detection, battery health monitoring — that became a core acquisition driver for IBM.
  • Built and led a team of 8+ data scientists and engineers; set technical direction, hiring standards, and delivery cadence across a platform monitoring 5–10 GW of renewable assets for enterprise IPP clients globally.
  • Owned the cross-functional relationship between data science, product, and engineering — driving problem discovery through to production deployment across a multi-client, multi-asset SaaS platform.
  • Defined the data architecture strategy for ingesting heterogeneous telemetry across multi-vendor SCADA systems — solving the fragmentation problem that had blocked fleet-scale analytics for operators, and setting the engineering standard the team executed against.
  • Grew the analytics function from a supporting capability into a core product differentiator — directly contributing to IBM's decision to acquire Prescinto.
Data ScienceAnalyticsData ArchitectureMachine LearningTeam Management

Utopus insights

R&D Engineer

Jun 2018May 2021 · 2 yrs 11 mos · Bengaluru

  • Conceived and built supervised deep learning models for wind turbine gearbox failure prediction — delivering 30+ day advance warning windows, productized and deployed across Vestas fleet operations covering hundreds of turbines.
  • Designed and deployed unsupervised anomaly detection (PCA + Q-residual analysis) for real-time turbine component health monitoring — moved from research prototype to production platform feature.
  • Led end-to-end forecasting architecture integrating NWP weather data with SCADA pipelines — physical and statistical ensemble models for day-ahead and intraday forecasting aligned to CERC regional compliance metrics.
Deep LearningPredictive MaintenanceAnomaly DetectionForecastingMachine Learning

Marlabs inc.

Data Scientist

Nov 2017Jun 2018 · 7 mos · Bengaluru

  • Built ML-based research analytics platform for a US investment firm — predicting high-return stocks from financial reports; selected as a case study at NASSCOM AI Game Changer Awards 2018.
  • Contributed to mAdvisor, an automated reasoning platform generating actionable insights from enterprise data without manual intervention.
Machine LearningResearchAnalytics

Reconnect energy

Research Analyst (ML)

Aug 2015Oct 2017 · 2 yrs 2 mos · Bengaluru

  • Built and deployed ML forecasting models for wind and solar generation at state and regional level — achieving 90–100% accuracy for solar and 75–95% for wind across real-time substation-level systems with 16 daily revision cycles.
  • Achieved sub-3% MAPE in electricity load forecasting by integrating weather and event-based features; designed automated retraining and calibration pipelines running without manual intervention.
  • Led IEX electricity price forecasting using ANN with seasonal trend modelling for day-ahead and intraday market behaviour.
Machine LearningForecastingData Analysis

Education

Veermata Jijabai Technological Institute (VJTI)

Master of Technology (MTech) — Control System

Aug 2013Jul 2015

Sant Gadge Baba Amravati University, Amravati

Bachelor of Engineering — Electronics and Telecommunication Engg

Aug 2008Jul 2012

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