Apply machine learning and Python to multi-sensor infrastructure data, developing models for railway defects, anomalies, and degradation. Work across cloud and edge systems.

As Lead Data Scientist, you will analyze noisy real-world data from point clouds, radar, imagery, and satellite deformation sources. You will build statistical and AI models for infrastructure monitoring and maintenance decisions. The work combines SQL, geospatial data, experimental design, and uncertainty quantification.

Key Responsibilities:

- Develop machine learning and deep learning infrastructure analysis methods
- Build algorithms detecting track, subsurface, surface, and vegetation exceptions
- Deploy AI models into onboard units and cloud platforms




- Forecast asset degradation and remaining service life
- Validate outputs against field records and regulatory thresholds

Requirements:

- Degree in quantitative discipline; master's or doctorate preferred
- Two or more years modeling noisy real-world data
- Robust Python and SQL programming skills
- Experience with sensor-derived data types
- Knowledge of validation and uncertainty quantification

Lead infrastructure analytics through Python, machine learning, sensor data, and validated predictions supporting railway maintenance decisions.

📌 Machine Learning Infrastructure Data Scientist (Toronto)
🏢 ApoSys Technologies
📍 Toronto