Data Scientist – Validation (energy Systems)

Stafford, ENG, GB, United Kingdom

Job Description

Summary


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GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life. Are you excited at the opportunity to electrify and decarbonize the world?



We are seeking a highly skilled and results-driven Data Scientist - Validation to join our team, primarily focusing on validating AI/ML models for grid innovation applications. This role will involve rigorous testing, validation, and verification of AI/ML models with grid data to ensure they meet accuracy, performance, and operational standards within energy systems. Reporting to the AI leader in the CTO organization, the Data Scientist will collaborate closely with Grid Automation (GA) product lines, R&D teams, product management, and other GA functions.



The ideal candidate will have significant experience in the energy sector, specifically in energy systems and grid automation, or in related domains such as smart infrastructure (e.g., connected buildings, utilities) or industrial automation (e.g., SCADA, PLC systems, Industry 4.0). They should have a strong understanding of how to apply data science and data engineering techniques to develop, validate, and enhance AI/ML models within these complex and data-rich environments.


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Essential Responsibilities:



Design and conduct experiments to test and validate AI/ML models in the context of energy systems and grid automation applications. Establish clear validation frameworks to ensure models meet required performance standards and business objectives. Establish test procedures to validate models with real and simulated grid data. Analyze model performance against real-world data to ensure accuracy, reliability, and scalability. Identify and address discrepancies between expected and actual model behavior, providing actionable insights to improve model performance. Implement automated testing strategies and pipeline to streamline model validation processes. Collaborate with Data Engineers and ML Engineers to improve data quality, enhance model performance, and ensure efficient deployment of validated models. Ensure that validation processes adhere to data governance policies and industry standards. Communicate validation results, insights, and recommendations clearly to stakeholders, including product managers and leadership teams.

Must-Have Requirements



PhD, Master's, or Bachelor's degree in Data Science, Computer Science, Electrical Engineering, or a related field with hands-on experience in model validation. Significant experience working in the energy sector, particularly in energy systems, grid automation, or smart grid technologies. Solid experience in validating AI/ML models, ensuring they meet business and technical requirements. Strong knowledge of statistical techniques, model performance metrics, and validation methodologies for AI/ML models. Proficiency in programming languages such as Python, R, or MATLAB. Experience with data wrangling, feature engineering, and preparing datasets for model validation. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model evaluation techniques. Experience with cloud platforms (e.g., AWS, Azure, GCP) and deployment of models in cloud environments. Experience with data visualization tools such as Tableau, Power BI, or similar to effectively present validation results and insights.

Nice-to-Have Requirements:



Familiarity with big data tools and technologies, such as Hadoop, Kafka, and Spark. Familiarity with data governance frameworks and validation standards in the energy sector. Knowledge of distributed computing environments and model deployment at scale. Strong communication skills, with the ability to clearly explain complex validation results to non-technical stakeholders.

At GE Vernova - Grid Automation, you will have the opportunity to work on cutting-edge projects that shape the future of energy. We offer a collaborative environment where your expertise will be valued, and your contributions will make a tangible impact. Join us and be part of a team that is driving innovation and excellence in control systems.

About

GEV

Grid Solutions:




At GEV Grid Solutions we are electrifying the world with advanced grid technologies. As leaders in the energy space our goal is to accelerate the transition for a more energy efficient grid to full fill the needs of tomorrow. With a focus on growth and sustainability GE Grid Solutions plays a pivotable role in integrating Renewables onto the grid to drive to carbon neutral. In Grid Solutions we help enable the transition for a greener more reliable Grid. GE Grid Solutions has the most advanced and comprehensive product and solutions portfolio within the energy sector.

Why we come to work:




At GEV, our engineers are always up for the challenge - and we're always driven to find the best solution. Our projects are unique and interesting, and you'll need to bring a solution-focused, positive approach to each one to do your best. Surrounded by committed, loyal colleagues, if you can dare to bring your ingenuity and desire to make an impact, you'll be exposed to game-changing, diverse projects that truly allow you to play your part in the energy transition.

What we offer:




A key role in a dynamic, international working environment with a large degree of flexibility of work agreements


Competitive benefits, and great development opportunities - including private health insurance.

Additional Information


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Relocation Assistance Provided:

No

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Job Detail

  • Job Id
    JD3118511
  • Industry
    Not mentioned
  • Total Positions
    1
  • Job Type:
    Contract
  • Salary:
    Not mentioned
  • Employment Status
    Permanent
  • Job Location
    Stafford, ENG, GB, United Kingdom
  • Education
    Not mentioned