Staff Analytics Engineer

Location: Greenville

Job Description Summary

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.

Essential Responsibilities

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.

• Work on a team of Analytics Engineers and Data Scientists who analyze equipment operation and failure modes to detect the failures through the analysis of process environment, equipment operation and real-time sensor data
• Apply a structured process to design and build new analytic packages. Review relevant technical documents
• Create derivative analytics for variants of known equipment, incorporation of field feedback to improve the performance and actionability of the analytics, maintaining the Analytic library and catalogue
• Write Analytics Functional Documentation for the Field Services teams, gather end-user requirements and support product engineering in development of new generations of GE Vernova Wind Solutions.
• Develop a trusted advocate relationship with internal and external customer constituents. Serve as the communications hub and "source of truth" for applications of the analytics in the field. Ensure that all teams have correct and timely information they need to ensure successful delivery of analytics
• Provide supporting technical materials, education and in person expertise during gate discussions, partner and cross-business collaboration. Evangelize the portfolio capabilities by presenting to customers (internal and external) and user groups where applicable
• Drive repeatability in solution component architectures. Foster an environment that encourages innovation, creative risk taking, speed and agility designing and managing Analytic Blueprint Packages

Basic Qualifications

• Bachelor’s degree in computer science or “STEM” Majors (Science, Technology, Engineering and Math) with minimum 4 years of experience with data analytics or data science.
• Minimum 2 years with Python and SQL

Additional Locations: Barcelona, Schenectady