Availability & Reliability Engineer

Roles and Responsibilities

• Downtime Diagnostics: Ingest and analyze massive datasets from the global wind fleet to identify the primary drivers of turbine unavailability.
• Root Cause Quantification: Quantify the impact of specific failure modes on overall fleet uptime, distinguishing between technical failures (Reliability) and service delays (Maintainability).
• Predictive Inventory Forecasting: Create data-driven projections for spare part demand to enable immediate return-to-service for down turbines without maintaining excessive or redundant inventory levels.
• Probabilistic Modeling: Develop and maintain component-level reliability models to predict the life cycle of critical turbine systems.
• Design Verification: Collaborate with design engineers to set reliability targets for new products and verify that replacement parts meet or exceed the reliability of failed components.
• Predictive Diagnostics: Assist in building advanced analytics aimed at early detection of degradation, shifting the fleet from reactive repairs to proactive, planned interventions.
• Strategic Projections: Build and refine statistical forecasts for fleet-wide availability to support long-term service agreements (LTSA) and financial risk management.
• Scenario Simulation: Model the impact of proposed fleet retrofits and corrective actions to forecast the expected "lift" in availability before capital is deployed.
• Growth Forecasting: Develop reliability expectations for next-generation turbine designs, ensuring that "as-shipped" products meet rigorous customer production guarantees.

Required Qualifications

• Bachelor’s degree in Mechanical Engineering, Electrical Engineering, or a related computational field (e.g., Systems Engineering or Data Science with an engineering focus).
• Strong foundation in probability and statistics.

Desired Characteristics

• Experience in Python, JMP/JSL, R, or SQL for engineering applications and data automation is highly preferred.
• Proven experience in reliability modeling, life-cycle analysis, or predictive performance forecasting for heavy-duty industrial assets.
• Ability to summarize complex technical data into clear, actionable conclusions for stakeholders across a global organization.
• At least 6 months of experience (including internships) in industrial products, preferably in wind energy or power generation.