- Salary
- $20 – $25
- Location
- Mississauga, ON
- Workplace
- Onsite
- Type
- Internship
- Department
- IT
- Experience
- 20+ years
- Education
- Master
- Closing date
- Today
- Source
- ApplyToJob
Description
About Us:
At Rodan Energy, our mission is Making Sustainable, Attainable. As a leading North American energy services company, we provide innovative energy solutions that help clients reduce energy costs and GHG emissions, support electrification, and enhance grid efficiency. With over 20 years of expertise, we’re trusted by North America’s largest energy users, power producers, and utilities.
Why Join Rodan:
Thriving Industry: Join a rapidly growing sector at the forefront of innovation and sustainability, helping shape the future of energy management.
Product Market Fit: With a 98% client retention rate and consistent year-over-year growth, Rodan has built a reputation for delivering trusted, long-term partnerships with industry leaders
Top Talent & Strong Culture: Collaborate with high-performing, passionate individuals who are committed to excellence, innovation, and sustainability.
What Makes This Role Great:
- Gain hands-on experience building forecasting models that inform real facility, market, and pricing decisions
- Exposure to time-series modeling, ML pipelines, and the Databricks platform in a live production environment
- Work alongside experienced data scientists and energy-market domain experts
- Opportunity to build technical skills in a collaborative, high-impact environment
- In-office collaboration, five days a week, designed for hands-on learning and mentorship
Key Responsibilities:
- Design, build, and validate time-series forecasting models for facility load, market demand, and electricity price signals
- Explore and tune modeling approaches (statistical, ML, and deep learning) with guidance from the team, based on the forecasting horizon, data availability, and accuracy requirements
- Contribute to ML pipelines on the Databricks platform using PySpark, MLflow, and Delta Lake (we’ll teach you the platform)
- Build dashboards and automated reports in Databricks to replace manual Excel workflows
- Learn from energy-market domain experts and incorporate their knowledge of 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
Required Skills & Qualifications
- Currently enrolled in a registered Co-op program and working towards 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 (e.g., pandas, scikit-learn, numpy)
- Foundational understanding of statistical or machine learning modeling concepts, ideally including time-series methods.
- Strong analytical thinking and clear written and verbal communication skills.
- Collaborative mindset, self-motivated, and comfortable asking questions and iterating on feedback
- Coursework, projects, or work experience related to the electricity sector and curiosity about the data and dynamics that drive it
- Familiarity with weather data integration for load and renewable forecasting
- Any exposure to the Databricks platform
- Familiarity with Git/GitHub for organizing and version-controlling your code
Term Details, Compensation & Work Location
- Term: September 2026 – December 2026 (4-month co-op term)
- Compensation: $20.00 - $25.00 per hour, based on year of study, relevant experience, and internal equity
- Schedule: 40-hour work week
- Location: Mississauga, ON
- Work Arrangement: 5 days per week in-office
- Vacancy Status: This posting is for an existing Fall 2026 co-op opportunity
At Rodan, we believe a great workplace goes beyond the work itself. Recognized as Canada’s Top Small & Medium Employer of 2026, we’ve built a culture rooted in collaboration, celebration, and giving back. Here’s what you can look forward to when you join the team:
- Celebrations Throughout the Year: We mark occasions and holidays all year long with team events including holiday parties, team dinners, bowling nights, go-karting, Annual Summer BBQ, golf tournaments, and more.
- Learning & Growth: Lunch and learns, crossword challenges, and education-funding programs to keep you sharp and growing in your career.
- Giving Back: Our Employee Initiatives Committee (EIC) organizes food drives and fundraising for local charities throughout the year – and everyone is welcome to get involved.
Apply Now: If you're a curious, driven student who's passionate about data, modeling, and the future of energy, we want to hear from you! Please submit your resume, cover letter, and unofficial transcript together as one attachment.
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Hiring Process
Rodan Energy's hiring process is designed to identify candidates who are the right fit — technically, culturally, and professionally. As part of our process, candidates should be aware of the following:
- Background Check: As part of our hiring process, the successful candidate will be required to complete a background check, which may include a criminal record check and credential verification. Professional references will also be collected as part of the process.
- Assignment: Shortlisted candidates may be asked to complete a practical assignment. This is an opportunity to demonstrate your problem solving approach, creativity, and how you think through a real-world dataset.
We respect your time and will provide clear instructions and reasonable timelines for any assignments requested.
Rodan Energy Solutions is committed to an inclusive, accessible recruitment and employment experience in compliance with the Accessibility for Ontarians with Disabilities Act, 2005 (AODA). Accommodations are available upon request throughout the hiring process and, if hired, during employment. If you require an accommodation, please contact [email protected] so we can work with you to meet your needs in a manner that respects dignity, independence, integration, and equal opportunity.
