Senior Data Scientist

Alert Labs is a certified B-Corporation, building technology solutions for good. We help our customers identify and eliminate water waste, water damage, reduce energy consumption, and ultimately lower their environmental impact. You can find our technology in commercial and retail buildings, condos, schools, construction sites, and many other applications across North America.

Did you know? By analyzing water data gathered by our easy-to-use sensors, Alert Labs customers have saved more than 55 billion litres of water and prevented 160,000 tonnes of CO2e from entering the atmosphere.

What kinds of activities will you do on our analytics team?

Join our innovative team as an intermediate or senior Data Scientist and play a pivotal role in elevating our HVAC analytics capabilities to new heights! Help transform noisy, real‑world HVAC sensor data streams into clear, actionable insights for customers. You’ll collaborate with our Mechanical Engineering team to uncover the physics‑based story behind the data, design and validate robust anomaly‑detection algorithms, and deploy them in production to power reliable alerts and diagnostics. Your work will directly improve customer outcomes and support our mission of reducing environmental impact by making home HVAC smarter.

This role is hybrid, with a couple of days per week in our Kitchener (soon to be Waterloo) office. Interviews will be a mix of remote and in‑person. Application review will start in the new year.

What you’ll do

• Analyze multivariate time‑series from HVAC systems (e.g., temperature, humidity, pressure, power, vibration) and connect signatures to real fault modes that an HVAC contractor or homeowner can understand and act upon.
• Partner with Mechanical Engineering to model expected system behavior (thermodynamics, heat transfer, psychometrics) and translate physics into features, constraints, and validation procedures.
• Build and validate anomaly‑detection algorithms using statistical and ML techniques; design experiments, benchmarks, and ground‑truthing plans.
• Productionize models with robust data pipelines, monitoring, and drift management; design for reliability, latency, and maintainability.
• Improve alert accuracy with clever ideas : sensor fusion, adaptive thresholds, debouncing, consensus ensembles, confidence scoring, and human‑in‑the‑loop review flows.
• Own data quality : detect / repair missing data, unit inconsistencies, calibration drift, understand and correct data issues that prevent quality analytics.
• Communicate clearly with engineering, peers, and leadership—turn findings into narratives, help recommend strategies and solutions.
• Mentor junior data scientists through code reviews, design docs, and learning sessions; model best practices in analysis and delivery.

Requirements

Nice-to-have experience

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