Machine Intelligence Analyst- P&HVAC

Sunbelt Rentals, Inc.•Fort Mill, SC

12d•$64,790 - $89,087•Onsite

About The Position

A Machine Intelligence Analyst focuses on utilizing advanced data analysis, artificial intelligence (AI), and machine learning (ML) to improve the performance, safety, and efficiency of heavy equipment. This role bridges the gap between raw telematics data—collected from sensors and CAN bus/Modbus systems—and actionable business intelligence by automating reporting and alerting to drive predictive maintenance, fuel savings, and operational optimization.

Requirements

Bachelor’s degree in data science, Engineering, Computer Science, or a related field; advanced degrees are often preferred for senior analyst roles.
Prior experience in the heavy civil construction or mining industries is highly desirable to understand specific machine behaviors and job site logic.
Strong problem-solving mindset and the ability to communicate technical findings to non-technical stakeholders, such as fleet managers and maintenance crews.
Experience with industrial communication protocols such as CAN bus and Modbus to collect data from machine control systems.
Proficiency in Python or R, along with libraries like TensorFlow, PyTorch, or Scikit-learn for building predictive models.
Strong understanding of GPS technology, onboard diagnostics (OBD), and Telematics Control Units (TCU) for remote monitoring.
Skills in SQL, data cleaning, and big data tools (e.g., Hadoop, Apache Spark) to handle large volumes of sensor data.
Ability to create complex queries and interactive dashboards using tools like Power BI, Tableau, or specialized construction software.

Nice To Haves

Advanced degrees are often preferred for senior analyst roles.

Responsibilities

Analyze sensor data (vibration, temperature, hydraulic pressure) to predict equipment failure, reducing downtime and lowering maintenance costs.
Extract, clean, and analyze raw data from CAN bus, Modbus, and GPS systems to monitor fleet health and location in real time.
Develop AI/ML models to improve fuel efficiency and optimize workflows (e.g., automated scheduling).
Create automated dashboards and proactive alerts for operators/managers regarding abnormal machine behavior.
Utilize ML to identify unsafe operator behavior (e.g., sudden braking, excessive idling) and ensure compliance with regulatory standards.
Develop and refine AI/ML models to identify anomalies in equipment performance (e.g., vibration spikes, temperature fluctuations) to predict failures before they occur.
Analyze machine utilization and idle time to recommend better asset allocation and reduce unnecessary operational costs.
Use AI to flag risky operator behaviors—such as harsh braking or overloading—and automate reporting for regulatory standards like OSHA.
Monitor consumption patterns and identify inefficient operating habits to drive fuel savings, often targeting reductions of up to 15%.
Build and maintain dashboards that provide real-time alerts for critical issues like engine faults or unauthorized equipment movement (geo-fencing).