1. Data Collection and Analysis: Gather and process large datasets from energy monitoring systems, IoT sensors, and other sources to create a comprehensive view of energy consumption. Perform in-depth analysis to identify usage patterns, inefficiencies, and areas for potential savings. 2. AI and Machine Learning Model Development: Design, implement, and optimize machine learning models to predict energy demand, production, forecast costs, and identify anomalies in energy usage. Develop AI-powered algorithms for automated energy control and real-time optimization based on consumption trends, real-time tariffs, and environmental data. 3. Automation of Energy Management Processes: Work closely with the automation team to design and deploy automated systems for energy management, including load balancing, scheduling, and smart grid interactions. Implement AI-driven solutions to enable self-regulating energy systems that respond dynamically to changes in consumption. 4. Reporting and Visualization: Create dashboards and data visualizations that communicate energy usage trends, efficiency metrics, and automated insights for stakeholders. Generate regular reports highlighting AI-driven findings, energy savings, and potential areas for improvement. 5. Collaboration and Cross-Functional Engagement: Partner with energy engineers, IT, and sustainability teams to align AI-driven insights with energy management objectives. Collaborate with technical teams to integrate AI solutions with existing energy management systems and IoT devices. 6. Continuous Improvement and Innovation: Stay current with advancements in AI, machine learning, and energy analytics to identify and implement cutting-edge solutions. Test and refine models and algorithms to improve their accuracy, scalability, and effectiveness in real-world energy applications.

Bachelor’s degree in Data Science, Computer Science, Engineering, Environmental Science, or a related field. 3+ years of experience in energy data analysis, with a focus on AI and machine learning applications. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch). Knowledge of energy management principles, IoT data sources, and smart grid technology. Skills and Competencies: Strong analytical skills with a proven ability to apply AI to complex energy datasets. Familiarity with automation tools and techniques for energy management. Excellent communication skills for presenting technical findings to diverse stakeholders.