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GE Vernova is seeking an experienced Staff Analytics Engineer to join our Wind Engineering FPM Data & Analytics team. You will develop scalable data and analytics solutions supporting wind turbine reliability, fleet performance, engineering investigations, and operational decision-making.
You will collaborate with engineering, digital technology, product, and operations teams to turn complex wind turbine and fleet data into trusted, scalable, and actionable solutions that improve product
GE Vernova is seeking an experienced Staff Analytics Engineer to join our Wind Engineering FPM Fleet Data & Analytics team. In this role, you will develop scalable data and analytics solutions that support wind turbine reliability, fleet performance, engineering investigations, and operational decision-making across our wind business.
You will be responsible for delivering new, re-usable analytics solutions to drive growth in GE Vernova Wind Services portfolio. You will work across a broad set of data sources, including SCADA, Condition based Monitoring Systems, Turbine and Sensor data, Operational Systems, and Enterprise Data, to build robust pipelines, analytics products, and monitoring capabilities. The ideal candidate combines strong technical depth in data engineering and analytics development with experience in platform design, DevOps practices, production support, and agile delivery.
This role requires close collaboration with engineering, digital technology, product, and operations teams to turn complex wind turbine and fleet data into trusted, scalable, and actionable solutions that improve product performance and customer outcomes.
As a Staff Analytics Engineer, for GE Vernova Wind's FPM Data & Analytics team, you must have a good understanding of basic Wind Turbine equipment - reliability and failure causes, ability to quickly understand the key operational characteristics of new equipment/ Component, collaborate with domain experts for failure mode knowledge, and assess how possible failure models will affect measured parameters and key performance indicators (KPIs). An Analytic Package is a complete configuration template for a class or type of equipment; includes the Sematic Data Model, Equipment Tag Model, Component Groupings, Failure Indication Logic and relation parameter configuration applied to Physics Based, Mathematical or Statistics based, or Machine Learning (Non-Parametric Regression Techniques such as Similarity Based Modeling, Principal Component Analysis, Kernel Regression, Support Vector Machines etc.; Decisions Trees and other such) analytics techniques commonly used for Industrial Analytics) as well as the orchestration logic of one or more of these techniques.