Responsibilities
- Support the development of smart maintenance initiatives using data analytics, AI, and machine learning technologies
- Support the collection, processing, and review of operational data to enable predictive maintenance and performance optimization
- Collaborate with internal and external stakeholders to identify key performance indicators, collect data, and analyze it to gain insights into system performance
- Develop dashboards and reports to visualize fleet performance and maintenance trends
- Support monitoring of project progress, coordinating with contractors and stakeholders, and addressing technical and site issues
- Explore and apply emerging technologies to enhance maintenance efficiency and reliability
Requirements
- Degree/ Higher Diploma in Computer Engineering, Electrical/Electronic Engineering, Data Science or a relevant discipline
- A minimum 4 years of relevant experience, including 2 years at supervisory level
- Proven experience with real-time monitoring, control, or condition monitoring systems
- Practical programming or scripting experience (e.g. Python, SQL, C++) and familiarity with cloud platforms e.g. AWS is a plus
- Strong analytical, communication, and problem-solving skills
- Fluent in written and spoken English and Cantonese, with professional proficiency in Putonghua
- Experience in railway or rolling stock systems is an advantage
Remarks
- Candidates with strong practical experience but lower academic qualifications may also be considered
Applications
You are invited to apply online via https://careers.mtr.com.hk/careersection/mtr_external/joblist.ftl?lang=en or send in your CV stating the position (with reference number) you are applying for by mail to Human Resource Management Department, MTR Corporation, G.P.O. Box 9916, Hong Kong on or before 28 May 2026.
For other job openings, please visit MTR Corporation's website for more details.
All information provided by applicants will be treated in strict confidence and used for recruitment purpose only. All personal data of unsuccessful applicants will be retained for 12 months for future recruitment purpose and will then be destroyed.
Schedule
Full-time
Job Posting
15/May/26, 12:00:00 AM
Closing Date
28/May/26, 11:59:00 PM
