Headline

Senior Data Science Professional | Digital Solutions | Product Development | PhD (AI) | IIMV | IEEE Senior Member

About

Delivering AI/ML Solutions for Industrial IoT: With over 10+ years of experience in condition monitoring, predictive maintenance, and energy management of industrial loads (including electrical machines, motors, generators, HVAC systems, AHUs, electrical loads, and utilities), I have built a strong foundation in applying advanced analytics to real-world industrial challenges. I am a data science professional with a PhD in Condition Monitoring and Predictive Maintenance of Electrical Machines using AI, with strong experience in applying machine learning and IoT technologies to industrial systems. I focus on leveraging real-time sensor data to develop anomaly detection models and condition monitoring algorithms that drive proactive maintenance, reduce downtime, and improve system efficiency. I lead a data science team, coordinating with data engineers and junior data scientists, while also managing cross-functional projects involving frontend, backend, and UI/UX teams. I work closely with global experts in data science and product innovation to bring intelligent, data-driven products to market. In recent roles, I have deployed edge-based machine learning algorithms for monitoring electrical equipment, built deep learning models for energy demand forecasting, and implemented AI-powered systems for anomaly detection in smart energy infrastructure. I’ve also contributed to the development and validation of edge-enabled non-intrusive load monitoring solutions through field trials and operational testing. My PhD work centered on building scalable fault diagnosis systems using machine learning, where I applied transfer learning to generalize from prototype systems to complex environments with limited fault data. I have filed two patents and published over 15 papers in international journals and conferences. Honors & Awards: I received the Early Career Award (2022) from the Prognostics and Health Management (PHM) Society, USA, in recognition of my contributions to predictive maintenance and machine learning in industrial applications. I was also selected as a Visiting Scholar (2018) for the prestigious Research Explorer Ruhr program in Germany, funded by the Excellence Initiative, which brought together outstanding early-career researchers from across the globe. Interests: My professional interests include condition monitoring, vibration analysis, predictive maintenance, energy management, signal processing, AI/machine learning, and data analytics, with a strong focus on applications in the manufacturing, energy, automotive, and related industrial sectors.

Location

Bengaluru

AI stack

claude code

langgraph

ollama

openai

claudecode

githubcopilot

Work experience

Data Scientist (Optimization) at SMA Solar India, Mar 2026 - Present · 7 mos, Bengaluru, Karnataka, India

Assistant Manager - Data Scientist (Digitalization) at Qlar Group (formerly Schenck Process), Jul 2025 - Mar 2026 · 9 mos, Bengaluru, Karnataka, India, • Lead digital transformation initiatives by integrating IoT data, diagnostics, and KPI development into operations to enable predictive maintenance and advanced analytics that improve reliability and efficiency. • Developed vibration signal-based gearbox and bearing fault-detection algorithms to reduce failures and prevent unplanned maintenance in process-industry machines. • Developed AI-based hyperspectral imaging systems for material detection in industrial processes. • Designed and implemented an end-to-end on-premise industrial data pipeline for real-time ingestion from PLC systems via OPC UA, with edge messaging using MQTT, Apache Kafka–based streaming, and local server storage and analytics. • Designed, built, and deployed a self-hosted RAG-based technical support system using enterprise knowledge sources. • Worked with global data science teams and product managers (Germany & UK) to design and deliver scalable digital solutions across the process industry.

Data Scientist at ebm-papst, Mar 2023 - Jul 2025 · 2 yrs 5 mos, Chennai, Tamil Nadu, India, • Worked on energy analytics and predictive maintenance for EC fan motors, developed AI-based algorithms for anomaly detection and vibration monitoring applications. • Leveraged IoT sensor data, statistical analysis, and unsupervised learning algorithms for real-time EC fan monitoring, enabling anomaly detection, proactive maintenance, and energy optimization. • Developed and deployed a vibration score–based algorithm for condition monitoring, improving reliability and extending equipment lifespan. • Led cross-functional teams and partnered with global data science & product innovation experts (Germany & US) to drive digitalization and scalable product development. • Successfully led a multifaceted projects by effectively coordinating with data engineers & data scientists. • Managed collaboration across various cross-functional teams to ensure seamless integration and delivery of project objectives.

Data Scientist at Atsuya Technologies, Oct 2021 - Mar 2023 · 1 yr 6 mos, Chennai, Tamil Nadu, India, • Developed and deployed edge-based algorithms for condition monitoring of electrical motors using current and vibration data, optimizing industrial operations. • Built a deep learning model for accurate energy demand forecasting in commercial buildings, leading to improved energy management strategies. • Created an analytical model for event detection in deep freezers, enhancing operational efficiency in retail stores. • Implemented an AI-powered anomaly detection system for smart energy meters, enabling improved energy management and reduced power losses. • Led a cross-functional project team, ensuring seamless collaboration between hardware, firmware, and UI/UX engineers.

Senior Project Associate at CSIR-Central Scientific Instruments Organisation, Oct 2020 - Sep 2021 · 1 yr, Chennai, Tamil Nadu, India, • Improved the performance of non-intrusive load monitoring (NILM) systems for smart and sustainable cities. • Deployed optimized ML/deep learning algorithms on hardware for real-time load energy monitoring. • Validated EdgeAI-based NILM performance through real-world field trials and case study analysis. • Led a cross-functional team by leveraging strong communication and project management skills.

Freelance Consultant at CSIR-Central Scientific Instruments Organisation, Apr 2020 - Oct 2020 · 7 mos, Chennai, Tamil Nadu, India, • Contributed to the enhancement of edge AI-based energy management by applying a non-intrusive load monitoring (NILM) approach, enabling device-level energy insights without the need for individual appliance sub-metering. • Authored comprehensive documentation and research articles, leading to successful publication of findings in reputed international journals and conferences.

Research Associate at CSIR-Central Scientific Instruments Organisation, Nov 2018 - Mar 2020 · 1 yr 5 mos, Chennai, Tamil Nadu, India, • Developed and deployed a non-intrusive load monitoring (NILM) system, incorporating machine learning algorithms for real-time monitoring, efficient event detection, and detailed energy consumption analysis, leading to improved energy optimization techniques. • Implemented machine learning algorithms on edge devices, ensuring operational effectiveness and reliability in real-time. • Led cross-functional projects, coordinating with embedded systems engineers and user experience specialists to achieve successful integration and deployment of solutions.

Education

Executive Post Graduate Certificate Program, General Management, Indian Institute of Management Visakhapatnam

Doctor of Philosophy (PhD), Condition monitoring of electrical machines using data driven (AI) approach, Amrita Vishwa Vidyapeetham