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Role Summary Develop analytical models and supporting data pipelines that identify potential Open Main conditions using AMI, network topology, and asset data. Support model development, validation, and operationalization activities. Key Responsibilities - Perform exploratory analysis of AMI and network datasets. - Engineer features from: - Customer interval usage - Voltage readings - Transformer relationships - Network topology - Asset metadata - Develop and test classification models under the guidance of the Lead Data Scientist. - Support graph construction and graph feature generation for network-based analytics. - Analyze model performance and investigate false positives and false negatives. - Build datasets required for training, validation, and testing. - Create visualizations and analytical summaries to support engineering review. - Document model logic, assumptions, and results. - Participate in model deployment and monitoring activities. Qualifications - Bachelor's or Master's degree in Data Science, Statistics, Engineering, Computer Science, or related field. - 5-8 years of experience in machine learning or advanced analytics. - Experience with: - Supervised classification techniques - Feature engineering - Time-series analysis - Anomaly detection - Graph analytics concepts - Proficiency in: - Python - SQL - Data visualization tools - Experience working with large-scale utility, IoT, sensor, or operational datasets preferred. Preferred Technical Skills - Random Forest - XGBoost - Gradient Boosting - Graph Neural Networks (preferred) - NetworkX or similar graph analytics libraries - Time-series feature extraction - Model explainability techniques (SHAP, feature importance, etc.) Intrested candidate can apply at Only immediate joiners to apply. .