Asset & Operational Intelligence Engineer

Asset Performance & Operational Intelligence Engineer

The Asset Performance & Operational Intelligence Engineer is responsible for transforming operational data from installed conveyor systems into actionable insights that improve customer OEE, reduce unplanned downtime, and inform maintenance and upgrade strategies throughout the equipment lifecycle.

This role serves as the analytical intelligence hub within Life Cycle Services—bridging system data, real-world operating conditions, and field execution. The position goes beyond reporting and dashboards, focusing on correlating system behavior to failure modes, identifying leading indicators of performance degradation, and driving data-backed corrective and preventive actions in the field.

Intralox is a division of Laitram, L.L.C., with an extensive portfolio of innovative conveyance solutions and services that improve lives and optimize businesses worldwide.

Our global workforce of over 3,000 employees in 20+ countries consist of reliable problem solvers, continuously developing and directly delivering solutions that have driven our customers’ growth worldwide for more than 50 years.

Intralox was founded on the principle of doing the right thing, by treating customers, employees, and suppliers with honesty, fairness, and respect. We invest heavily in these values and aim to practice our business philosophy principles every day, which is why we have been consistently recognized for innovation and workplace excellence. We believe in the power of a good idea no matter where it comes from, using trust as the foundation to how we work, and that self-managed people are our greatest asset.

We listen objectively, think creatively, and deliver results. To learn more about our company culture, philosophy, and benefits, please visit our company page.

This position is not eligible for work authorization or sponsorship.

• Own the interpretation and breakdown of OEE metrics for installed conveyor systems
• Analyze Availability, Performance, and Quality losses at system, subsystem, and zone levels
• Identify and prioritize chronic vs acute performance loss drivers
• Validate that measured OEE reflects true operational behavior and customer reality

• Correlate time-series data including (but not limited to):
  • Motor current, temperature, vibration
  • Conveyor speed and load variations
  • Fault, alarm, and recovery behavior
• Identify leading indicators of failures and performance degradation
• Develop repeatable pattern recognition for common failure modes across the installed base

• Translate data trends into actionable maintenance recommendations
• Support the shift from time-based to condition-based maintenance strategies
• Provide data-driven input for inspection priorities, component replacement, and spares strategy
• Partner with field service and maintenance teams to validate interventions and outcomes

• Identifying systems at risk before failures occur
• Prioritizing service interventions based on impact and risk
• Quantifying the value of recommended adjustments or upgrades

• Identify data quality gaps, noisy signals, or incorrect system configurations
• Work with product controls engineering and integration engineering to improve data reliability at the source
• Provide structured feedback to engineering teams to improve future system designs and standards

• Produce clear, concise insight summaries—not just dashboards—focused on:
  • What is happening
  • Why it is happening
  • What should be done next
• Present findings to LCS leadership, engineering teams, and customer-facing stakeholders
• Track outcomes of recommendations and validate…