Job Description

Job Title: Research Engineer – Data-Driven Energy Efficiency Analytics

Job Summary

We are seeking a Research Engineer to support the development of data-driven energy analytics solutions for industrial manufacturing systems. The candidate will be responsible for developing energy disaggregation algorithms, energy monitoring and analytics,functions to improve visibility, benchmarking, and optimization of energy use across manufacturing equipment and processes.

The role will involve working with energy meter data, machine/process condition data, IoT systems, and AI/ML models to quantify energy consumption, identify inefficiencies, detect anomalies, and support decision-making for energy efficiency improvement and sustainable manufacturing.

Key Responsibilities

• Develop data-driven energy disaggregation models to estimate machine-, process-, and batch-level energy consumption from aggregated or limited metering data.
• Design and implement energy monitoring, analytics and benchmarking algorithms for energy performance tracking, anomaly detection, and causal analytics related to energy-saving of industrial equipment and manufacturing processes.
• Conduct experiments, model validation, and performance evaluation using industrial datasets.
• Work closely with project partners, engineers, and researchers to translate research outcomes into deployable industrial solutions.

Requirements

• Bachelor’s or Master’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, Data Science, Industrial Engineering, or a related field.
• Strong programming skills in Python, with experience in data analytics, machine learning, and time-series processing.
• Knowledge of energy systems, power consumption analysis, industrial equipment, or manufacturing processes.
• Experience with machine learning techniques for regression, classification, anomaly detection, forecasting, or signal disaggregation.
• Ability to process and analyze large-scale time-series datasets.
• Good problem-solving skills and ability to work independently in applied research and development projects.
• Good written and verbal communication skills.

Preferred Qualifications

• Experience in non-intrusive load monitoring, energy disaggregation, virtual sensing, or industrial energy analytics.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience with causal analytics.
• Knowledge of manufacturing systems, additive manufacturing, CNC, heat treatment, pumps, compressors, motors, or other industrial equipment.
• Prior experience in applied R&D projects with industry partners is an advantage.