Data Scientist - Validation (Energy Systems)

GE Vernova; Stafford; On-site; GBP 40,000 - 70,000; Full time

Job summary

GE Vernova is looking for a Data Scientist - Validation to validate AI/ML models for grid innovation applications. This role involves testing and verifying models to ensure they meet accuracy and performance standards, collaborating with various teams in a dynamic environment focused on energy transition.

Qualifications

• Experience in model validation within the energy sector.
• Proven ability to validate AI/ML models.
• Strong knowledge of statistical techniques and validation metrics.

Responsibilities

• Design experiments to test and validate AI/ML models in energy applications.
• Develop test procedures with real and simulated grid data.
• Collaborate with Data and ML Engineers to enhance data quality.

Skills

Model validation

Statistical techniques

Data wrangling

Feature engineering

Data visualization

Education

Degree in Data Science

Degree in Computer Science

Degree in Electrical Engineering

Tools

Python

R

MATLAB

TensorFlow

PyTorch

Scikit-learn

AWS

Azure

GCP

Tableau

Power BI

Job description

Job Description Summary

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

We are seeking a skilled Data Scientist - Validation to join our team, focusing on validating AI/ML models for grid innovation applications. The role involves testing, validation, and verification of AI/ML models with grid data to ensure they meet standards in accuracy, performance, and operations. Reporting to the AI leader in the CTO organization, the Data Scientist will collaborate with Grid Automation (GA), R&D, product management, and other GA functions.

The ideal candidate will have experience in the energy sector, particularly in energy systems, grid automation, or related fields like smart infrastructure or industrial automation. They should understand how to apply data science techniques to develop, validate, and improve AI/ML models in complex, data-rich environments.

Job Responsibilities

• Design experiments to test and validate AI/ML models in energy and grid applications.
• Establish validation frameworks to meet performance and business objectives.
• Develop test procedures with real and simulated grid data.
• Analyze model performance to ensure accuracy and scalability.
• Identify and resolve discrepancies, providing insights for improvement.
• Implement automated testing pipelines for model validation.
• Collaborate with Data and ML Engineers to enhance data quality and model deployment.
• Ensure validation processes comply with data governance and industry standards.
• Communicate results and insights effectively to stakeholders.

Minimum Requirements

• Degree in Data Science, Computer Science, Electrical Engineering, or related field with experience in model validation.
• Experience in the energy sector, especially energy systems and grid automation.
• Proven experience validating AI/ML models to meet requirements.
• Strong knowledge of statistical techniques and validation metrics.
• Proficiency in Python, R, or MATLAB.
• Experience with data wrangling, feature engineering, and dataset preparation.
• Familiarity with 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 Skills

• Knowledge of big data tools such as Hadoop, Kafka, Spark.
• Understanding of data governance and validation standards in energy.
• Experience with distributed computing and large-scale deployment.
• Strong communication skills for explaining complex validation results.

Join GE Vernova Grid Automation to work on innovative projects shaping the future of energy. We offer a collaborative environment, growth opportunities, and a chance to impact the energy transition.