Data Scientist – Grid Innovation Model Development

GE Vernova
Stafford
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist Placement

Job Title and Company

Data Scientist – Grid Innovation Model Development at GE Vernova

Overview

We are looking for a passionate, creative, and results-driven Data Scientist with solid experience in validating AI/ML models, typically gained through at least 5 years working within the energy sector or related domains such as smart infrastructure or industrial automation. The ideal candidate has a strong track record of independently leading and delivering AI/ML model validation projects in complex, data-rich environments.

As part of our AI & Grid Innovation team, you will be at the forefront of testing, verifying, and validating cutting-edge AI/ML models specifically designed for grid innovation applications. You will play a key role in designing and building robust validation frameworks that ensure AI/ML solutions meet stringent accuracy, performance, and operational standards across both edge and cloud environments. This role reports to the AI Lead within the CTO organization and offers a unique opportunity to collaborate closely with Grid Automation product lines, R&D teams, product management, and other business units. You will contribute to creating impactful, sustainable, and inclusive solutions across energy systems, smart infrastructure, and industrial automation.

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 pipelines 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.
  • Proven experience in the energy, smart infrastructure, or industrial automation sectors, with deep expertise in system protection, automation, monitoring, and diagnostics, typically acquired through a minimum of 5 years within a multinational manufacturing company. 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.
Location and Relocation

Relocation Assistance Provided: No

About GE Vernova Grid Solutions

At GE Vernova Grid Solutions we electrify the world with advanced grid technologies. We focus on growth and sustainability and play a pivotal role in integrating renewables onto the grid to drive toward carbon neutrality. We help enable the transition for a greener, more reliable grid and offer a comprehensive product and solutions portfolio within the energy sector.

Why We Come To Work

Our engineers tackle challenging, diverse projects and bring a solution-focused, positive approach. You will be part of a collaborative environment with committed colleagues, where your ingenuity and impact are valued.

What We Offer

A dynamic, international working environment with flexibility in work arrangements; competitive benefits and development opportunities, including private health insurance.

Job Function and Industries
  • Engineering and Information Technology
  • Electric Power Generation
Seniority and Employment Type
  • Mid-Senior level
  • Full-time

Note: This description reflects the job responsibilities and requirements at the time of posting and is subject to change.


#J-18808-Ljbffr

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 Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.