Key Responsibilities and Required Skills for Wind Turbine Analyst

🎯 Role Definition

The Wind Turbine Analyst is a specialist who combines domain knowledge of wind turbine technology with strong data analysis skills to monitor fleet performance, diagnose faults, recommend corrective actions, and drive continuous improvement across operations and maintenance (O&M). This role analyzes SCADA and condition monitoring data, develops performance and reliability metrics, supports root cause investigations, and partners with operations, engineering, and OEMs to improve energy yield and reduce downtime.

📈 Career Progression

Typical Career Path

Entry Point From:

Wind Turbine Technician / Field Service Technician transitioning into data and performance analysis
SCADA Analyst or SCADA Engineer with experience in turbine telemetry and alarms
Data Analyst or Reliability Engineer within energy, utilities, or heavy industry

Advancement To:

Senior Wind Turbine Analyst / Lead Performance Engineer
Asset Performance Manager / Fleet Performance Lead
Reliability Manager or O&M Operations Manager
Data Science Lead for Renewable Energy or Head of Asset Optimization

Lateral Moves:

Condition Monitoring Engineer / Vibration Analyst
SCADA Systems Engineer or Control Systems Specialist
Grid Integration / Power Systems Analyst

Core Responsibilities

Primary Functions

Monitor, process, and analyze SCADA and condition monitoring data across multiple wind farms to detect performance deviations, energy losses, and equipment anomalies, producing actionable daily and weekly reports for O&M teams.
Lead root cause investigations for underperformance and repeated faults by correlating alarm logs, controller events, SCADA trends, vibration spectra, oil analysis, and maintenance records to produce clear technical findings and remediation actions.
Develop, validate and maintain performance models and power curves for individual turbines and entire wind farms, including site-specific corrections for wind shear, turbulence intensity, icing, and curtailment to quantify energy losses and gains.
Design and implement predictive maintenance algorithms and reliability models (including failure rate analysis, MTBF/MTTR, and Weibull or hazard models) to prioritize inspection and replacement activities and reduce unplanned downtime.
Create and maintain automated dashboards, KPIs, and visualizations (availability, capacity factor, equivalent availability, alarm rates, downtime classification) using tools such as Power BI, Tableau, or Python-based visualization libraries to support operations and executive decision-making.
Perform advanced time-series analysis and anomaly detection on SCADA streams using Python or R (pandas, numpy, statsmodels), and collaborate with data scientists to deploy machine learning models for fault classification and remaining useful life estimation.
Produce defect and defect-cost analysis for gearboxes, generators, blades, pitch systems, and hydraulic subsystems by integrating inspection reports, OEM guidance, warranty status, and repair cost estimates to support financial planning and vendor negotiations.
Coordinate with field service teams and OEM technical support to prioritize intervention strategies, issue work packages, and validate repair effectiveness post-maintenance by comparing pre- and post-fix performance metrics.
Maintain and improve the SCADA data pipeline and data quality processes: perform data cleansing, gap-filling, timestamp alignment, and meta-data management to ensure reliable analytics foundations.
Execute loss-of-production (LOP) and curtailed energy analysis, attributing lost energy to causes such as grid curtailment, planned maintenance, site wakes, turbine derating, and environmental constraints; quantify impact financially.
Evaluate and implement condition monitoring systems (CMS) hardware and software upgrades, define alarm thresholds, and tune detection logic to reduce false positives while maintaining early detection sensitivity.
Support lifetime extension and repowering studies by analyzing component condition trends, structural load histories, and fatigue accumulation to inform reinvestment decisions and retirement timing.
Conduct blade health monitoring through combined SCADA, lidar, and inspection data to detect leading/trailing edge erosion, lightning strike impacts, and structural damage; recommend repair or inspection intervals.
Develop and document standard operating procedures (SOPs), analytical playbooks, and investigation templates for consistent troubleshooting, ensuring repeatable, auditable methodologies across the analyst team.
Lead cross-functional investigation meetings with stakeholders from operations, health & safety, engineering, and commercial teams, presenting technical findings and proposed mitigation plans in a clear, non-technical format for decision-makers.
Support warranty claims and OEM performance investigations by preparing evidence packages, time-synchronized SCADA trends, event logs, and regression analyses to substantiate defect or performance claims.
Run sensitivity and scenario analyses for curtailment strategies, grid events, and meteorological extremes to inform bidding, forecasting, and risk mitigation for both day-to-day operations and strategic planning.
Ensure compliance with industry standards (e.g., IEC 61400 series) and incorporate regulatory and permitting constraints into operational analyses and reporting for both onshore and offshore assets.
Assist in the development and refinement of asset-specific fault trees and failure mode, effects, and criticality analysis (FMECA) to support reliability-centered maintenance and spare parts optimization.
Provide hands-on support for site commissioning and acceptance testing by verifying SCADA signal integrity, performing sensor calibration checks, and certifying performance tests against contract guarantees and power curves.
Mentor junior analysts and technicians in data analysis techniques, SCADA best practices, and interpretation of performance metrics, building team capability and ensuring high-quality investigations across the fleet.
Maintain awareness of OEM controller updates, firmware changes, and industry best practices to proactively adjust monitoring strategies and ensure analytics remain aligned with current turbine behavior.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis for commercial, forecasting, and engineering teams, delivering clear, documented outputs and reproducible scripts.
Contribute to the organization's data strategy and roadmap by identifying analytics use-cases, required telemetry improvements, and tooling investments that increase automation and insight velocity.
Collaborate with business units to translate data needs into engineering requirements, including specifications for additional SCADA channels, CMS sensors, and edge-compute solutions.
Participate in sprint planning and agile ceremonies within the analytics and engineering teams; estimate effort, deliver features, and prioritize technical debt related to data quality and model maintenance.
Support procurement and evaluation of third-party analytics platforms, condition monitoring vendors, and digital twin providers by defining technical requirements and assessing vendor deliverables against performance KPIs.
Participate in health, safety, and environmental (HSE) reviews by analyzing incident logs and generating safety-related insights derived from operational anomalies or sensor alerts.
Assist asset managers in preparing board-level performance summaries, contractual compliance reports, and insurance-related loss evaluations that require detailed technical substantiation.
Facilitate knowledge transfers and training sessions for operations personnel on interpreting analytics dashboards, alarm prioritization, and condition indicators to improve front-line decision making.

Required Skills & Competencies

Hard Skills (Technical)

SCADA Data Analysis: Deep experience extracting, cleaning, aggregating, and interpreting SCADA telemetry streams and controller event logs across multiple turbine OEMs (e.g., Vestas, Siemens Gamesa, GE).
Condition Monitoring & Vibration Analysis: Familiarity with CMS systems, vibration spectra interpretation, envelope analysis, and fault signatures for bearings, gearboxes, and generators.
Programming & Scripting: Proficient in Python (pandas, numpy, scikit-learn), R, or MATLAB for data processing, modeling, and automation of analytical workflows.
Time-Series & Anomaly Detection: Strong skills in time-series methods, filtering, FFT/PSD analysis, and anomaly detection techniques applied to telemetry and sensor data.
SQL & Relational Databases: Advanced SQL for querying large telemetry and maintenance databases; experience with timeseries databases and data warehouses.
Data Visualization & Reporting: Experience building actionable dashboards and KPI reports using Power BI, Tableau, Grafana, or similar tools with a focus on operational clarity.
Power Curve & Wake Loss Modeling: Practical knowledge of performance modeling, power curve fitting, site calibration, and wake loss estimation methods.
Reliability Engineering & Statistical Analysis: Use of reliability metrics (MTBF, MTTR), failure mode analysis, Weibull distributions, and lifetime/cost modeling to inform maintenance strategies.
Machine Learning & Predictive Models: Exposure to supervised and unsupervised learning methods for fault classification, RUL prediction, and predictive maintenance use-cases.
Familiarity with Turbine Systems: Solid technical understanding of turbine subsystems (blades, pitch, yaw, gearbox, generator, converter, hydraulic/pneumatic systems) and primary failure modes.
CMS/SCADA Tools & Protocols: Hands-on experience with SCADA historians, OPC, IEC 61850/61400 protocols, and common CMS vendor platforms.
Advanced Excel & Statistical Tools: Skilled in complex Excel modeling, VBA macros, and statistical packages for exploratory analysis and ad-hoc reporting.
GIS & Meteorological Data Integration: Ability to interpret met mast/lidar data, integrate meteorological inputs, and use GIS for site-level spatial analysis.
Cloud & Big Data Platforms (preferred): Familiarity with AWS/Azure data services, containerized deployments, or batch-processing frameworks for large-scale telemetry pipelines.
Standards & Compliance: Knowledge of IEC 61400, local grid codes, health & safety compliance, and environmental constraints relevant to wind farm operation.

Soft Skills

Strong communication skills: Able to translate complex technical findings into concise, business-focused recommendations for non-technical stakeholders.
Stakeholder management: Experience coordinating cross-functional teams including field technicians, OEM engineers, commercial managers, and regulators.
Problem-solving and analytical mindset: Methodical approach to root cause analysis and the confidence to test hypotheses with data-driven evidence.
Attention to detail: Rigorous data validation habits, meticulous documentation, and commitment to reproducible analyses.
Prioritization and time management: Ability to triage multiple investigations and balance reactive fault response with proactive reliability projects.
Teamwork and mentoring: Willingness to coach junior staff, share best practices, and contribute to a collaborative continuous improvement culture.
Commercial awareness: Understanding of how technical recommendations impact revenue, warranty, insurance, and O&M budgets.
Adaptability & learning agility: Keeps current with evolving analytics tools, turbine technologies, and industry best practices.
Presentation & storytelling: Comfortable presenting findings to executives and investors, crafting succinct narratives supported by visuals and datasets.
Resilience under pressure: Effective decision-making during outages or high-impact incidents with clear, prioritized action plans.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Mechanical Engineering, Electrical Engineering, Renewable Energy Engineering, Data Science, Statistics, or a related technical discipline.

Preferred Education:

Master’s degree in Wind Energy, Renewable Energy Systems, Reliability Engineering, Data Science, or an MBA with technical background.

Relevant Fields of Study:

Mechanical Engineering
Electrical/Electronic Engineering
Renewable Energy / Wind Energy
Data Science / Statistics
Physic or Applied Mathematics

Experience Requirements

Typical Experience Range:

2–7 years of hands-on experience in wind turbine performance, SCADA analysis, condition monitoring, or reliability engineering.

Preferred:

5+ years of experience within wind O&M, OEM technical support, asset management, or a consulting environment with demonstrable impact on energy yield, availability improvements, or O&M cost reductions.
Experience working with multiple turbine OEMs and across onshore and offshore asset classes, with proven track record of leading cross-functional technical investigations and delivering measurable outcomes.