National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Scientist – Energy Systems Validation (Energy Sector Experience Required)

GE Vernova
Stafford
1 week ago
Create job alert

Job Description Summary
GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, helping our customers power economies and deliver vital electricity for health, safety, and security. Are you excited about the opportunity to electrify and decarbonize the world?

We are seeking a highly skilled and results-driven Data Scientist - Validation to join our team, focusing on validating AI/ML models for grid innovation applications. This role involves 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 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 related domains such as smart infrastructure or industrial automation. They should have a strong understanding of applying data science and engineering techniques to develop, validate, and improve AI/ML models in complex, data-rich environments.

Essential Responsibilities:
Design and conduct experiments to test and validate AI/ML models in energy systems and grid automation.
Establish validation frameworks to ensure models meet performance standards and business objectives.
Develop test procedures to validate models with real and simulated grid data.
Analyze model performance against real-world data for accuracy, reliability, and scalability.
Identify discrepancies between expected and actual model behavior, providing insights for improvement.
Implement automated testing strategies and pipelines to streamline validation processes.
Collaborate with Data and ML Engineers to enhance data quality and model deployment.
Ensure validation processes adhere to data governance policies and standards.
Communicate validation results and insights clearly to stakeholders.
Must-Have Requirements:
Degree in Data Science, Computer Science, Electrical Engineering, or related field with experience in model validation.
Experience in the energy sector, especially in energy systems or grid automation.
Proven experience validating AI/ML models to meet requirements.
Strong knowledge of statistical techniques and validation methodologies.
Proficiency in Python, R, or MATLAB.
Experience with data wrangling, feature engineering, and dataset preparation.
Knowledge of machine learning frameworks like TensorFlow, PyTorch, Scikit-learn.
Experience with cloud platforms (AWS, Azure, GCP) and deploying models.
Ability to use data visualization tools like Tableau or Power BI.
Nice-to-Have:
Familiarity with big data tools like Hadoop, Kafka, Spark.
Knowledge of data governance and validation standards in energy.
Experience with distributed computing and large-scale deployment.
Strong communication skills for explaining complex validation results.
At GE Vernova - Grid Automation, you'll work on innovative projects shaping the future of energy, in a collaborative environment that values your expertise.

About GEV Grid Solutions:
We are leaders in advanced grid technologies, accelerating the transition to a more energy-efficient and renewable-powered grid. Our focus is on growth and sustainability, helping to integrate renewables and achieve a carbon-neutral future.

Why work with us:
Our engineers face unique challenges, with projects that allow you to bring ingenuity and make an impact. Join a team of committed colleagues working on diverse, game-changing projects in the energy transition.

What we offer:
A dynamic, international environment with flexible work arrangements, competitive benefits, and development opportunities, including private health insurance.

Additional Information:
Relocation Assistance Provided: No
#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Senior Data Scientist - Voice

Senior Data Scientist - Voice

Data Scientist Sydney, Australia

Data Scientist

Data Scientist

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.