Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop predictive maintenance tools and optimise workflows using data science techniques.
- Company: Join Hitachi Rail, a leader in digital transformation and technology.
- Benefits: Competitive salary, performance bonuses, 25 days holiday, and flexible perks.
- Why this job: Make a real impact on train operations and service quality with cutting-edge technology.
- Qualifications: Degree in Engineering, strong Python coding skills, and experience in data analysis.
- Other info: Inclusive workplace with excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Join to apply for the Maintenance Analytics Engineer role at Hitachi Rail.
About Us
A career at Hitachi Rail will help create a legacy. With operations in every corner of the world, our work goes to the cutting‑edge of digital transformation and technology. From the multi‑cultural strength of our global organisation to the sustainable and innovative ways we work to bring people together, there’s something for everyone to get stuck into.
Description
Here at Hitachi Rail, we have a fantastic and unique opportunity for an experienced Maintenance Analytics Engineer to join the team. Based from our London HQ, working on a hybrid basis, the experienced Maintenance Analytics Engineer will be responsible for improving 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 Based Maintenance (CBM) methodology and Data Science Techniques. The Maintenance Analytics Engineer will play a critical role in connecting field operations with the maintenance organisation, helping minimise downtime and failure rates and maximise train operation.
The successful candidate ideally would come from either an Electronics or Software engineering background but must have strong experience coding in Python and exposure to data science technologies.
Responsibilities
- 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.
- Implement prognostic algorithms with different proprietary technologies and coding languages such as Pyspark, Python, PowerBI.
- Analyse diagnostics and maintenance data.
- 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.
About You
- Engineering Degree, preferably Electrical or Electronics Engineering.
- Experience in railway domain (preferred) or other manufacturing industry such as avionics, automotive or R&D.
- Experience in coding with Python, SQL, preferably Pyspark, and versioning tools (SVN, Git).
- Familiar with PowerBI dashboard development.
- Able to interpret electrical drawings, system specification software specification (e.g., UML) and mechanical drawings.
- Experience in prediction/CBM algorithms.
- Experience in statistical analysis including quality control, multiple linear regression models, logistic regression, discriminant analysis, re‑sampling techniques.
- Experience in big data analysis techniques both unsupervised and supervised machine learning.
- Good understanding of RCM methodology.
- Able to write technical specification for software and electronic systems.
- Able to manage relationship with suppliers and end‑customers.
- Ability to communicate effectively both orally and in writing.
- An understanding of health and safety requirements of a working environment.
Desirable
- Experience in maintenance of vehicle railways equipment.
- Experience in design of vehicle railway system.
What We Offer
- Competitive salary.
- Annual Performance bonus paid on discretionary basis.
- 25 days holiday.
- Pension scheme with contributions up to 9%.
- Private medical insurance.
- Personal Accident insurance.
- Group Income protection.
- Group Life Insurance.
- Employee Assistance Programme.
We also offer additional perks for you to choose from within a flexible plan that will meet your specific needs and lifestyle.
Thank you for your interest in Hitachi Rail. If your application is of interest, we will be in contact.
Equal Opportunity
At Hitachi Rail, there is a place for everyone. We welcome and value differences in background, age, gender, sexuality, family status, disability, race, nationality, ethnicity, religion, and world view. It is our commitment to create an inclusive environment - we are proud to be an equal opportunity employer.
Predictive Maintenance Engineer employer: Hitachi Rail
Contact Detail:
Hitachi Rail Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Predictive Maintenance Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at Hitachi Rail on LinkedIn. Ask them about their experiences and any tips they might have for landing the Maintenance Analytics Engineer role. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for the interview by brushing up on your Python skills and data science techniques. Be ready to discuss how you've used these in past projects. Show us how you can apply your knowledge to improve workflows and optimise maintenance processes.
✨Tip Number 3
Don’t just focus on technical skills; highlight your ability to communicate effectively. As a Maintenance Analytics Engineer, you'll need to connect with various teams. Share examples of how you've successfully collaborated in the past.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining Hitachi Rail. Don’t miss out on this fantastic opportunity!
We think you need these skills to ace Predictive Maintenance Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Predictive Maintenance Engineer role. Highlight your experience with Python, data science techniques, and any relevant projects that showcase your skills in maintenance analytics.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about the railway industry and how your background in electronics or software engineering makes you a perfect fit for the team at Hitachi Rail.
Showcase Your Technical Skills: Don’t forget to mention your coding experience, especially with Python and Pyspark. If you've worked on any predictive algorithms or data analysis projects, be sure to include those details to catch our eye!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining the Hitachi Rail family!
How to prepare for a job interview at Hitachi Rail
✨Know Your Tech
Make sure you brush up on your coding skills, especially in Python and Pyspark. Be ready to discuss your experience with data science techniques and how you've applied them in previous roles. This will show that you're not just familiar with the tools, but that you can use them effectively.
✨Understand RCM and CBM
Familiarise yourself with Reliability Centred Maintenance (RCM) and Condition Based Maintenance (CBM) methodologies. Be prepared to explain how you've implemented these practices in past projects and how they can improve service quality and equipment reliability.
✨Showcase Your Analytical Skills
Highlight your experience with statistical analysis and big data techniques. Be ready to discuss specific examples where you've used regression models or machine learning to solve problems or optimise processes. This will demonstrate your ability to analyse data effectively.
✨Communicate Clearly
Effective communication is key, especially when connecting field operations with maintenance teams. Practice explaining complex technical concepts in simple terms, as you'll need to convey your ideas clearly to both technical and non-technical stakeholders.