Senior Applied Scientist (ML Predictive Maintenance, Asset Intelligence)

Join our team as a Senior Applied Scientist, where you will play a key role in transforming frontline operations through AI/ML and Industrial IoT. You will design, develop, and optimize machine learning models for fault detection and classification, conduct experiments to optimize model performance, and collaborate with domain experts to ensure real-world application alignment. This position offers flexible time off, training and development investments, competitive salary and equity opportunities, and comprehensive healthcare coverage.

- Design, develop, and optimize machine learning models for fault detection and classification, including data and training modeling choices. - Perform exploratory data analysis (EDA) on vibration, operational technology (OT), and time-series data to uncover insights and identify patterns indicative of faults or anomalies. - Collaborate with product managers and domain experts to validate findings, ensure alignment with real-world applications, and prioritize product features.

- An expert level of English, both spoken and written, is required, as the individual will need to lead platform engineering managers across multiple teams, present technical priorities to executive stakeholders, and align with engineering leaders outside Québec on a daily basis - Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on condition monitoring or machine learning applications - Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation - 5+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc - Familiarity with time-series modeling techniques and feature engineering - Strong foundational knowledge in machine learning, data science, and statistics - Hands-on experience developing models for OT and vibration analysis, condition monitoring, and fault detection or classification - Familiarity with signal processing techniques (e.g., Fourier transforms, wavelet analysis) and their application to OT and vibration data