About the Role
You will be a vital part of the asset management team, focusing on the development of predictive algorithms and prognostics. Your work will directly support decision-making in predictive maintenance for railway systems and digital asset management. Your responsibilities will encompass machine learning, mathematical/statistical analysis, coding, data ingestion/engineering, and system engineering, all aimed at achieving optimal asset management and maintenance outcomes throughout the asset lifecycle.
You will collaborate closely with various teams across LTA, rail operators, and system manufacturers to manage data and interfaces for rail assets. A key aspect of your role will be to articulate rail asset management business use cases through data. Furthermore, you will be involved in reviewing technical submissions and participating in the testing and commissioning of predictive and analytical decision support systems.
For those in senior positions, there will be an expectation to provide technical leadership within the team, particularly in areas such as statistics, data science, MLOps, and data engineering. Mentoring junior data scientists and analysts will be a significant contribution.
Requirements
- Tertiary qualification in Electrical, Electronics Engineering, Computer Science, Mechanical, Industrial Engineering, Sustainable Infrastructure Engineering, or an equivalent field.
- Strong writing, presentation, coding, communication, and interpersonal skills.
- A team player with strong critical and logical thinking capability, capable of working independently.
- Experience in (a) IT application, data analytics/machine learning, OR (b) System Engineering or Railway Engineering using data will be an advantage.
- Candidates with relevant years of experience will be considered for senior positions. For senior positions, the following are considered essential:
- Solid understanding of statistical analysis and predictive modelling / machine learning.
- Knowledge of data management, data engineering, and data visualization techniques.
- Experience with implementing MLOps in an enterprise IT system.
- Ability to lead and mentor junior data scientists / analysts.