Senior Data Scientist - AI for Rail Ops & Maintenance

LTA Land Transport Authority; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Engineering the intelligence layer of LTA's Railway Common Data Platform, you will design and deploy ML solutions that detect anomalies in railway time-series data and provide actionable insights for maintenance and operations. The role blends deep technical work with a focus on delivering tangible value.

You will translate real-world use cases into data problems, build end-to-end models from discovery to monitoring, and collaborate with cross-functional teams to improve rail performance and

Qualifications

• Degree in Data Science, Statistics, Computer Science, Engineering, or related quantitative field.
• At least 2 years of proven relevant experience in data science.
• Experience in transportation, IoT, or large-scale condition-monitoring systems with AI agents is advantageous.
• Willingness to undertake technical assessments or case studies during interviews.
• Medical declaration and/or further assessment may be required during shortlisting.

Responsibilities

• Build the 'intelligence layer' of the CDP to detect anomalies and provide actionable outcomes.
• Develop end-to-end ML solutions from data discovery to deployment and monitoring.
• Translate use cases into data problems and generate value for decision making.
• Collaborate with cross-functional teams including maintenance and operations.

Skills

Python

SQL

Time-series analysis

Anomaly detection

MLOps

Education

Degree in Data Science, Statistics, Computer Science, Engineering, or related quantitative field

Tools

Snowflake

Databricks

APIs

Job description

Engineering the intelligence layer of LTA's Railway Common Data Platform, you will design and deploy ML solutions that detect anomalies in railway time-series data and provide actionable insights for maintenance and operations. The role blends deep technical work with a focus on delivering tangible value.

You will translate real-world use cases into data problems, build end-to-end models from discovery to monitoring, and collaborate with cross-functional teams to improve rail performance and