About the role

Machine Learning Resident assisting in AI and ML projects for energy consumption modeling and forecasting at Enerva. Collaborating with experts to develop predictive models and analytical tools.

Responsibilities

This is a paid residency that will be undertaken over a 12-month period with the potential to be hired by our client, Enerva, afterwards (note: at the discretion of the client).
The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities.
Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development.
Design, implement, optimize, and evaluate machine learning models to support energy consumption forecasting and related analytical tasks.
Prepare, clean, and preprocess high-quality datasets to ensure they are suitable for training or fine-tuning, validating, and comparing forecasting models.
Apply state-of-the-art modeling techniques, ML frameworks, tools and open-source libraries to improve model performance, accelerate workflows, and optimize data processing.
Undertake applied research on ML and time-series techniques to improve or extend existing forecasting approaches.
Contribute to improving ML pipelines with a focus on efficiency, scalability, and real-time processing capabilities.
Collaborate with the project team and stakeholders to develop proof-of-concept and MVP-level solutions aligned with the client.
Engage in regular client meetings, contributing to presentations and reports on project progress.

Completion of a Computer Science (or a related graduate degree program) MSc. or PhD with specialization in time-series analysis, or energy forecasting applications.
Proficient in developing, training, fine-tuning, and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow, including model tuning and performance optimization.
Proficient in Python and common ML frameworks, libraries, and toolkits (e.g., Scikit-learn, LMStudio, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace), including data cleaning, preprocessing, and feature engineering for modeling workflows.
Solid understanding of classical statistics and its application in model evaluation, validation, and performance assessment.
Familiarity with Linux, Git version control, and writing clean code.
A positive attitude towards learning and applying machine learning techniques in a new applied domain.
Must be legally eligible to work in Canada.

Work under the mentorship of an Amii Scientist for the duration of the project
Participate in professional development activities
Gain access to the Amii community and events
Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)
Build your professional network
The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion)