Posted:
12/16/2025, 4:00:00 PM
Location(s):
Cambridge, England, United Kingdom ⋅ England, United Kingdom
Experience Level(s):
Internship
Field(s):
AI & Machine Learning ⋅ Data & Analytics
Project Overview:
Developing and refining enterprise data models that integrate commercial, operational, and financial data
Building machine learning–based electrical power outlook forecasts (demand, pricing, and generation patterns)
Supporting margin forecasting models to improve profitability insights and decision-making.
Internship Outcomes:
Contribute to an enterprise data model that connects customer, operations, and financial data for analytics use cases
Prototype and evaluate one or more ML models for electrical power outlook forecasting (e.g., load, price, or renewable generation)
Assist in enhancing or developing a margin forecasting model (e.g., at deal, product, or portfolio level) with clear performance metrics
Document data definitions, model assumptions, and analytical workflows for handoff to the broader team.
Primary Skills Developed:
Enterprise Data Modeling:
Conceptual, logical, and/or physical data modeling
Understanding of relational databases, data warehousing concepts, and dimensional modeling
Machine Learning & Forecasting:
Time series analysis and regression techniques
Model evaluation (e.g., MAE, RMSE, MAPE)
Analytics & Visualization:
Data exploration, feature engineering, and basic visualization to explain model behavior
Technical Tools (typical examples):
Programming: Python or R
Data: SQL, familiarity with data warehouses or cloud data platforms
ML/Analytics libraries: pandas, scikit-learn, statsmodels (or equivalents)
Basic Qualifications:
Currently pursuing a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or related quantitative field.
Minimum GPA of 4.0/5.0.
Other Eligibility Requirements:
Unrestricted work authorization in US is required for the duration of the internship.
Desired Qualifications:
Fundamental knowledge of:
SQL and at least one programming language (preferably Python or R)
Basic statistics (distributions, correlation, hypothesis testing)
Introductory machine learning concepts (regression, supervised learning)
Ability to work with structured datasets, perform data cleaning, and create reproducible analyses
Strong analytical thinking, attention to detail, and ability to communicate findings clearly in written and verbal form
Benefits Available to you:
Addressing the climate crisis is an urgent global priority, and at GE Vernova, we take our responsibility seriously. That is the singular mission of GE Vernova: to continue electrifying the world while simultaneously working to help decarbonize it. In order to meet this mission, we provide varied, competitive benefits to help support our workforce: Our Culture | GE Vernova (gecareers.com)
The pay for this position ranges from $24-36/hr. based on years of undergraduate/graduate field of study completed.
This position is also eligible for:
General Electric Company, Ropcor, Inc., their successors, and in some cases their affiliates, each sponsor certain employee benefit plans or programs (i.e., is a Sponsor”). Each Sponsor reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a Sponsor’s welfare benefit plan or program. This document does not create a contract of employment with any individual.
Inclusion
At GE Vernova, we believe in the value of your unique identity, background and experiences. We are committed to fostering an inclusive culture, where everyone feels empowered to do their best work because they feel accepted, respected and that they belong. Click here to learn more: https://jobs.gecareers.com/vernova/global/en/i-d-e
This position will remain open until at least January 5, 2026.
Website: https://www.gevernova.com/
Headquarter Location: Boston, Massachusetts, United States
Employee Count: 10001+
Year Founded: 2021
IPO Status: Public
Last Funding Type: Grant
Industries: Energy ⋅ Energy Efficiency ⋅ Sustainability