Type: Full-time
Location: In person – Kitchener, Ontario
Team: R&D / ML
Swish Solar is building self-cleaning solar panel technology to keep panels dust- and snow-free, reduce maintenance costs, and increase energy yield in harsh environments. We are also developing a real-time analytics platform for utility-scale solar farms that uses SCADA, inverter, weather, satellite, and operational data to detect losses, estimate soiling impact, and recommend better cleaning and maintenance decisions.
We are looking for an Applied ML Engineer / Data Science Engineer to join our R&D team and build reliable algorithms for solar performance analysis, soiling detection, and maintenance optimization. This role combines physics-guided reasoning, statistics, and machine learning to work with messy real-world plant data and help move models into production pipelines, APIs, dashboards, and customer-facing analytics.
We are looking for someone who can combine strong data science and machine-learning skills with practical engineering judgment. The ideal candidate is comfortable working with messy real-world solar farm data, understands time-series modeling and anomaly detection, and can use physical reasoning alongside data-driven methods.
This person should be able to build reliable models, explain their assumptions clearly, write clean and maintainable code, and help move algorithms from research notebooks into production-ready analytics.
This is a full-time, in-person role based in Kitchener, Ontario.
As part of the hiring process, selected candidates will complete a take-home technical project in the second round.
#J-18808-LjbffrData Scientist • Kitchener, Region of Waterloo, Canada
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