Applying engineering and business concepts to design and develop analytical models and statistical techniques for mission critical datasets.
Develop and implement KPI calculations, baselines, thresholds, and aggregations for time-series and event-based data.
Work with and analyze facility operations datasets, including maintenance logs, system hierarchies, sensor data, and industry standards.
Prototype and build reports/dashboards.
Use AI-assisted development tools (e.g., Claude, Cursor, Copilot, etc.) to accelerate prototyping, data gathering, and test generation
Develop reusable analytics libraries.
Validate accuracy of analytical models and libraries using real-world data.
Document and maintain modules, components, and scripts for relevant software.
Contribute to developing and refining features in existing datasets to enable future AI & advance analytics.

Pursuing an Undergraduate or Master’s degree in Engineering (Mechanical, Electrical, Industrial), Facilities Management, Computer Science, Data Science
Preference towards candidates with engineering knowledge of energy efficiency, HVAC systems, electrical systems
Strong attention to detail and structured problem-solving skills.
Ability to apply systems thinking and engineering rigor to data problems (inputs, outputs, dependencies, failure modes).
Strong interest in working with time-series data, IoT, or industrial/asset performance analytics.
Working knowledge of Python, SQL, KQL, and/or AI assisted programming tools (Claude, Copilot, etc.)
Exposure to business intelligence tools (Power BI, Tableau) and/or cloud analytics platforms (Microsoft Fabric, Azure Data Explorer, or similar) a plus.
Knowledge of energy efficiency, HVAC, or electrical systems in facilities a plus.

Engineering Analytics Intern

Key skills