About the role
Applied ML Engineer / Data Science Engineer – Solar Analytics
Type: Full-time Location: In person – Kitchener, Ontario Team: R&D / ML About Swish Solar 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.
About the Role
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.
Responsibilities
Build and maintain robust solar data pipelines for SCADA, inverter, MPPT, irradiance, weather, and operational time-series data, including cleaning, alignment, anomaly detection, and quality control. Develop solar performance analytics, soiling detection, energy-loss attribution, forecasting, and maintenance optimization models using physics-guided and data-driven methods. Design validation, backtesting, uncertainty scoring, data-quality gates, and model-monitoring workflows to ensure reliable production performance. Collaborate with software engineers to deploy model outputs through APIs, batch or streaming pipelines, dashboards, reports, and customer-facing analytics.
Required Skills
Strong Python and machine-learning skills, including NumPy, pandas, scikit-learn, time-series workflows, model selection, validation, interpretability, and error analysis. Experience with time-series modeling, signal processing, feature extraction, seasonality, change-point detection, anomaly scoring, and uncertainty estimation. Ability to work with imperfect telemetry data, including missing values, sensor faults, outliers, timestamp issues, drift, and non-stationary behavior. Ability to combine data-driven modeling with physical reasoning, write clean and documented code, and communicate assumptions, limitations, and modeling decisions clearly.
Nice to Have
Experience with solar, renewable energy, weather, satellite, or environmental time-series data. Familiarity with PV performance concepts such as irradiance, performance ratio, specific yield, temperature effects, curtailment, clipping, trackers, and solar position. Experience with physics-guided ML, hybrid modeling, anomaly detection, fault classification, or predictive maintenance. Experience with production ML workflows, including model monitoring, drift detection, retraining, optimization, scheduling, and deep-learning frameworks such as PyTorch.
What We Are Looking For
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.
Not the right fit? Search for Data Scientist jobs in Kitchener, Ontario, Canada
About Swish Solar
Solar panels lose up to 60% of their efficiency from sand and snow buildup. Current cleaning methods are expensive, water-intensive, and often ineffective, wasting billions of liters of water every year. Most solar farms are still cleaned on fixed schedules rather than when it’s actually needed, leading to millions in lost potential revenue and unnecessary operating costs.
Swish Solar is building the ecosystem for efficient and sustainable solar. Our Swish film is a self cleaning nanotech film that removes sand and snow without water or moving parts, restoring lost efficiency. Paired with SwishOS, our AI platform that analyzes soiling and determines the optimal cleaning schedule, we help solar operators maximize energy yield and revenue.
We are making solar energy smarter, cleaner, and truly sustainable, unlocking the full potential of renewable energy worldwide.
Similar Jobs
About the role
Applied ML Engineer / Data Science Engineer – Solar Analytics
Type: Full-time Location: In person – Kitchener, Ontario Team: R&D / ML About Swish Solar 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.
About the Role
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.
Responsibilities
Build and maintain robust solar data pipelines for SCADA, inverter, MPPT, irradiance, weather, and operational time-series data, including cleaning, alignment, anomaly detection, and quality control. Develop solar performance analytics, soiling detection, energy-loss attribution, forecasting, and maintenance optimization models using physics-guided and data-driven methods. Design validation, backtesting, uncertainty scoring, data-quality gates, and model-monitoring workflows to ensure reliable production performance. Collaborate with software engineers to deploy model outputs through APIs, batch or streaming pipelines, dashboards, reports, and customer-facing analytics.
Required Skills
Strong Python and machine-learning skills, including NumPy, pandas, scikit-learn, time-series workflows, model selection, validation, interpretability, and error analysis. Experience with time-series modeling, signal processing, feature extraction, seasonality, change-point detection, anomaly scoring, and uncertainty estimation. Ability to work with imperfect telemetry data, including missing values, sensor faults, outliers, timestamp issues, drift, and non-stationary behavior. Ability to combine data-driven modeling with physical reasoning, write clean and documented code, and communicate assumptions, limitations, and modeling decisions clearly.
Nice to Have
Experience with solar, renewable energy, weather, satellite, or environmental time-series data. Familiarity with PV performance concepts such as irradiance, performance ratio, specific yield, temperature effects, curtailment, clipping, trackers, and solar position. Experience with physics-guided ML, hybrid modeling, anomaly detection, fault classification, or predictive maintenance. Experience with production ML workflows, including model monitoring, drift detection, retraining, optimization, scheduling, and deep-learning frameworks such as PyTorch.
What We Are Looking For
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.
Not the right fit? Search for Data Scientist jobs in Kitchener, Ontario, Canada
About Swish Solar
Solar panels lose up to 60% of their efficiency from sand and snow buildup. Current cleaning methods are expensive, water-intensive, and often ineffective, wasting billions of liters of water every year. Most solar farms are still cleaned on fixed schedules rather than when it’s actually needed, leading to millions in lost potential revenue and unnecessary operating costs.
Swish Solar is building the ecosystem for efficient and sustainable solar. Our Swish film is a self cleaning nanotech film that removes sand and snow without water or moving parts, restoring lost efficiency. Paired with SwishOS, our AI platform that analyzes soiling and determines the optimal cleaning schedule, we help solar operators maximize energy yield and revenue.
We are making solar energy smarter, cleaner, and truly sustainable, unlocking the full potential of renewable energy worldwide.