Teck Resources

Over eight months, I led and supported data analysis initiatives aimed at improving the health, maintenance, and efficiency of mining equipment. My work bridged engineering and data science, helping reduce downtime and optimize preventative maintenance and operations in a high-intensity industrial setting. My contributions included:

• Predictive Maintenance & Equipment Monitoring – Developed automated alerts and analytics for mining rope shovels using InfluxDB visualization tools, enabling real-time equipment health monitoring.

• Cost & Downtime Reduction – Implemented early failure detection strategies, that helped prevent stranded equipment and unscheduled downtime, generating an estimated annual savings of CAD$150,000 within Elk Valley alone.

• Data Analysis & Process Improvement – Applied statistical analysis and confusion matrices to refine on-site heavy equipment alerts, improving accuracy and minimizing false positives.

• Operational Efficiency – Collaborated with maintenance teams and data scientists to refine existing alert systems, so maintenance could be scheduled before failures occurred.

Working in one of the largest steelmaking coal operations in the world gave me valuable insight into predictive maintenance, industrial-scale data analytics, and process optimization. Much like motorsport and automotive engineering, mining required innovative solutions that balanced reliability, safety, and cost efficiency. My time at Teck strengthened my ability to integrate analytics with mechanical systems, sharpened my problem-solving skills, and reinforced the importance of data-driven engineering in high-stakes industrial environments.