Engineering Services Senior Analyst
Designation: Engineering Services Senior Analyst
Qualifications:BE/BTech/Master of Engineering
Years of Experience:5 to 8 years
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What would you do? We are seeking a skilled Data Scientist / Predictive Modelling Engineer with experience in asset health analytics and OCR integration for electricity transmission assets. The ideal candidate will develop predictive models to support proactive maintenance and reliability planning for cables, LV systems, and transformers, using Python and OCR technologies to extract insights from both structured and unstructured data sources.
What are we looking for? • Exp in predictive modelling / data science, ideally in the utilities or energy sector. • Strong proficiency in Python and key libraries: pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch. • Experience with OCR libraries and tools: for document/image processing. • Solid background in machine learning, time series analysis, and anomaly detection. • Familiarity with electrical asset health indicators and degradation modes (e.g., transformer oil data, insulation aging). • Experience integrating unstructured data into predictive models. • Strong communication skills and the ability to present technical findings to non-technical stakeholders. • Experience with cloud platforms (AWS, Azure, GCP) and cloud-native OCR services. • Experience with EAM systems like SAP PM or IBM Maximo. • Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, or related field.
Roles and Responsibilities: • Develop and maintain predictive models to assess and forecast asset health, failure risk, and remaining useful life (RUL). • Apply OCR techniques to extract data from inspection reports, engineering drawings, and maintenance records. • Work with domain experts to incorporate engineering knowledge into models for cables, LV systems, transformers, and other transmission assets. • Analyze condition monitoring data (e.g., thermal, oil analysis, partial discharge, DGA Analysis) and identify degradation patterns. • Build machine learning and time-series forecasting models to detect anomalies and predict failures. • Design data pipelines to handle both structured sensor data and unstructured text/images (via OCR). • Communicate model outputs through visualizations, dashboards, and technical documentation. • Collaborate across asset management, engineering, and data teams to implement model-driven strategies.
Bengaluru
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