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 Robust Python and machine-learning skills, including Num Py, 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, and 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.

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📌 Data Scientist (Kitchener)
🏢 Swish Solar
📍 Kitchener