The Data Scientist will improve service quality and equipment reliability by developing tools and systems for improving workflows and optimising maintenance processes using suitable practices, Reliability Centred Maintenance (RCM), Condition Base Maintenance (CBM) methodology and Data Science Techniques.
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The Data Scientist will play a critical role in connecting field operations with the maintenance organisation, helping minimise downtime and failure rates and maximize train operation.
- Review and design main train subsystems with focus on maintainability, availability, reliability and CBM rule application
- Develop prognostic algorithms, principles based on its logic, signals/events/operational information within related resolution and sampling timing and prescriptions for maintenance
- Develop models for telemetry/diagnostic streams (e.g., anomaly detection, forecasting, survival/RUL) in Python and SQL, orchestrated on Kubernetes with Kubeflow Pipelines.
- Analyse diagnostics and maintenance data and create operational dashboards (e.g., Power BI, Grafana) to support maintainers and engineers
- Monitor prognostic system performances and statistical analysis on collected data to identify critical trends and conditions.
- Support prognostic algorithm verification and validation. This will include simulations with TCMS simulator and historical diagnostic data, analysis of maintenance reports and on field failures.
- Study and review of the vehicle FMECA/FMEA and maintenance plan
- Support and participate in RCM design activities.
- Master the communication among the diagnostic MMI interfaces, TCMS, the specific on-board subsystem controllers and on-ground system
- Write and review maintenance procedures and plans, train users on CBM..
- To undertake any other reasonable duties and responsibilities as may be required
Requirements
- At least one year experience in railway domain (preferred) or other manufacturing industry such as avionics, automotive or R&D
- Experience in coding with Python, SQL, and versioning tools (SVN, Git)
- Familiar with PowerBI and Grafana dashboard development
- Able to interpret electrical drawings, system specification software specification (e.g., UML) and mechanical drawings
- Build, run and monitor production code (e.g., on Kubernetes) using pipelines, reusable components, and scheduling
- Experience in time-series/forecasting models for anomaly detection, RUL/survival analysis and CBM algorithms.
- Experience xrdztoy in statistical analysis including quality control, regression models, re-sampling techniques and error/false‐alarm minimization in operational contexts
- Experience in big data analysis techniques both unsupervised and supervised machine learning and deep learning
- Good understanding of RCM methodology
- Able to write technical specification for software and electronic systems
- An understanding of health and safety requirements of a working environment
Qualifications
- Degree in Engineering or Computer science
Desirable Requirements
- Experience writing production‐ready Python code and building Grafana dashboards (Preferred)
- Experience querying large datasets with optimized SQL (Preferred)
- Experience in maintenance of vehicle railways equipment (Preferred)