Develop and deploy machine learning models to optimize HVAC setpoints, energy consumption, and operational performance.
Design algorithms for predictive maintenance, fault detection, and dynamic thresholding specific to chillers, pumps, cooling towers, and air handling units.
Work with domain experts and controls engineers to integrate data-driven solutions into existing BMS, SCADA, or edge computing platforms.
Analyze historical and real-time sensor data (temperature, pressure, flow, energy) to identify patterns, anomalies, and optimization opportunities.
Build scalable pipelines for data ingestion, cleaning, normalization, and feature engineering using time-series data from HVAC systems.
Conduct what-if analyses and energy simulations to validate model outputs and savings estimates.
Create visualizations, dashboards, and reports that clearly communicate insights and recommendations to technical and non-technical stakeholders.
Collaborate with software engineers to productize algorithms within cloud or on-prem solutions.
Requirements :
Bachelor's or Master's degree in Data Science, Computer Science, Mechanical Engineering, Energy Systems, or related field.
3+ years experience applying data science techniques for industrial systems, preferably with HVAC or energy optimization projects.
Strong skills in Python / R / SQL, with libraries such as pandas, scikit-learn, TensorFlow/PyTorch, XGBoost, Neural Network and RL model development.
Proven experience with time-series analysis, forecasting, and anomaly detection.
Knowledge of HVAC equipment, control strategies, and energy efficiency principles.
Hands-on experience with BMS/SCADA/OPC/Modbus/OPC UA data integration.