The Senior Systems Engineer – Predictive Fleet Intelligence is the engineering authority responsible for defining how fleet health is monitored, how developing equipment failures are detected, and how engineering knowledge is transformed into scalable digital solutions.

Serving as the domain expert for predictive fleet intelligence, this role establishes the engineering requirements, monitoring strategies, detection logic, alerting philosophy, and technical specifications that enable Software Engineering and Data Science teams to develop predictive monitoring applications, fleet intelligence products, and operational decision-support tools.

Rather than developing software or analytics models directly, this engineer defines what should be monitored, why it matters, how abnormal behavior should be identified, and what actions should result. Success is measured by reducing unplanned downtime, improving fleet reliability, increasing early fault detection, and continuously expanding the organization’s predictive engineering capabilities.

Working across Engineering, Reliability, Operations, Controls, Technical Services, Digital Solutions, and Data Science, this role transforms operational experience, root cause investigations, and equipment expertise into reusable engineering standards that improve the performance of an entire fleet of industrial assets.

Primary Deliverables

This role is responsible for developing and continuously improving the engineering products that enable predictive fleet intelligence, including:

• Fleet Health Monitoring Specifications defining monitoring objectives, required data sources, operating thresholds, engineering logic, and alert behavior.

• Predictive Detection Rule Specifications that enable Software Engineering and Data Science teams to develop automated monitoring applications and predictive analytics.

• Failure Pattern & Leading Indicator Rules Library documenting confirmed failure signatures, detection criteria, engineering rationale, probable causes, and recommended operational responses.

• Predictive Alert Specifications defining alert context, response guidance, escalation logic, notification strategies, and acceptance criteria.

• Fleet Health Dashboard & Operational Intelligence Specifications defining the engineering requirements for dashboards, reports, and digital decision-support products.

• Standardized Alert Response Packages enabling field operations and technical support teams to investigate and resolve predictive alerts without requiring engineering involvement.

• Alert Effectiveness Reports & Continuous Improvement Recommendations measuring monitoring performance, operational outcomes, false-positive trends, and opportunities to improve predictive monitoring capabilities.

Key Responsibilities

Fleet Health Monitoring Strategy & System Design

• Define the engineering strategy for monitoring the health and performance of complex industrial assets, including control systems, power systems, mechanical equipment, supporting infrastructure, communications networks, and deployed software configurations.

• Author detailed monitoring requirements that define what equipment should be monitored, what operating conditions represent normal performance, and what deviations indicate degradation or impending failure.

• Establish system health logic, engineering thresholds, operating limits, and classification criteria that distinguish healthy operation from abnormal conditions.

• Define data acquisition requirements by identifying the signals, parameters, instrumentation, and operational data necessary to support meaningful equipment health assessment.

• Develop engineering specifications for predictive monitoring capabilities, documenting leading indicators, failure signatures, and detection logic that enable early identification of developing equipment failures.

• Develop equipment-specific monitoring standards and predictive detection strategies for major asset classes across the fleet, defining how healthy operation, degradation, and impending failure manifest within operational data.

• Define leading indicators and engineering detection criteria that enable developing equipment failures to be identified sufficiently in advance to support proactive operational interven