Asset Performance & Operational Intelligence Engineer

Laitram•Baltimore, MD

3d•$78,000 - $194,600•Onsite

About The Position

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.

Requirements

Bachelor's degree in Mechanical Engineering, Electrical Engineering, or other related field.
5+ years experience in one or more of the following: Industrial systems analytics, Manufacturing or material handling performance analysis, Reliability or asset performance engineering
Strong working knowledge of: Conveyor systems and industrial automation concepts, OEE and related performance metrics, Maintenance and reliability principles (MTBF, downtime analysis, failure modes)
Travel up to 50%
Strong SQL skills and experience working with time-series data
Proficiency in at least one analytical language or tool (Python, R, or equivalent)
Experience with data visualization platforms (Power BI, Tableau, Grafana, etc.)
Ability to interpret PLC-derived signals and system states (no programming required, but fluency in meaning is essential)
Strong systems-thinking and problem decomposition skills
Comfortable working across engineering, maintenance, service, and customer-facing teams
Able to translate technical analysis into clear operational recommendations
Hands-on mindset—willing to engage with field teams and real-world system behavior
Curious, structured, and outcome-oriented

Nice To Haves

Experience in material handling, logistics automation, or manufacturing systems
Exposure to CMMS or EAM platforms
Understanding of condition monitoring techniques
Ability to interpret PLC programming (LD,FB,SFC,ST).
Experience supporting installed equipment across multiple customer sites
Familiarity with service-based or aftermarket business models

Responsibilities

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, Accumulation logic states, 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
Identify systems at risk before failures occur
Prioritize service interventions based on impact and risk
Quantify the value of recommended adjustments or upgrades
Enable proactive customer engagement using performance insights
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 performance improvements