Time Series Machine Learning Intern Schneider Electric

Based in Andover, MA, Schneider Electric’s Artificial Intelligence (AI) hub team aims to deliver world class AI products to solve customer challenges by enhancing digital offers with AI driven insights and new kinds of AI products addressing Energy, Sustainability and Industrial Automation domains, all leveraging a comprehensive Platform approach.

We’re looking for a passionate and product‑minded Time Series Machine Learning Intern to join us in Summer 2026.

The candidate will help in developing data science products based on time series machine learning algorithms using IoT data. The position will report to the Senior Principal ML/AI Architect in the team but will work closely with respective offer managers and usecase squads.

• Develop Python-based time series machine learning algorithms for fleetwide IoT data, including anomaly detection and classification.
• Create frameworks for prototyping and benchmarking machine learning algorithms and tools on data across fleets of similar industrial assets.
• Select appropriate datasets and data representation methods
• Run machine learning tests and experiments to support the product development
• Develop and test strategies for training and retraining machine learning systems
• Research and survey industrial reports and technical papers on latest industrial trends, case studies, models and methodologies.

For this U.S. based position, the expected compensation range is $22.50 – $39.50 per hour. In addition, this position is eligible for overtime pay and recognition programs.

The compensation rate for this position is for candidates located within the United States. Individual pay is determined by several factors including knowledge, job‑related skills, experience, and relevant education or training.

• The candidate must be currently enrolled in a masters or doctoral graduate program in a related discipline such as Mathematics, Statistics, Physics, Engineering, Data Science or CS
• Demonstrable experience in full stack data science, including developing and deploying machine learning models in a production environment
• Experience working with time series data is required. Prior experience with Python-based libraries for time series, such as SKTime, tsfresh, Ruptures will be an advantage.
• Familiarity with time series machine learning algorithms/ frameworks like ROCKET, Matrix profile techniques, contrastive learning, and architectures like Inception Time and other 1D Convolutional Neural Networks will be a plus!
• Familiarity with time series foundational models, like MOMENT, TimeGPT and Granite will be a plus!
• Experience exploring and training models with big datasets, using cloud technologies (especially Databricks)
• Proficient in scientific Python (Num Py, Sci Py, scikit‑learn, Pandas, XGBoost), SQL, and version control (Git Hub)
• Ability to formulate hypotheses, draw conclusions and deliver results
• Excellent verbal and written communication and interpersonal skills
• Ability to present findings to stakeholders and communicate results to all colleagues