About the role#
Rodan Energy Solutions Inc. is seeking a Data Science Co-op Student for the Fall 2026 term. This role offers the opportunity to work alongside experienced data scientists and domain experts to build forecasting models that inform real-world facility, market, and pricing decisions. You will gain hands-on experience with time-series modeling and ML pipelines in a live production environment.
What you'll do#
- Design, build, and validate time-series forecasting models for facility load, market demand, and electricity price signals.
- Explore and tune statistical, machine learning, and deep learning modeling approaches with team guidance.
- Contribute to ML pipelines on the Databricks platform using PySpark, MLflow, and Delta Lake.
- Build dashboards and automated reports in Databricks to replace manual Excel workflows.
- Incorporate knowledge of energy market structures, tariff rules, and grid dynamics into model features and evaluation frameworks.
- Communicate model performance, assumptions, and limitations to both technical and non-technical stakeholders.
What you'll need#
- Current enrollment in a registered Co-op program working toward an undergraduate or graduate degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field.
- Working knowledge of Python, SQL, Excel, and core data science libraries such as pandas, scikit-learn, and numpy.
- Foundational understanding of statistical or machine learning modeling concepts, ideally including time-series methods.
- Strong analytical thinking skills and the ability to communicate clearly in written and verbal formats.
- A collaborative and self-motivated mindset with a willingness to ask questions and iterate based on feedback.
Location & details#
- Location: Mississauga, Ontario, Canada.
- Work Modality: On-site, 5 days per week.
- Term: Fall 2026 (September 2026 – December 2026).
- Commitment: Full-time (40 hours per week).
- Compensation: $20.00 - $25.00 per hour, based on year of study, relevant experience, and internal equity.
