Data Scientist Intern

Description de l'offre

Alstom

The Railway industry today is characterized by both a strong and sustained growth across the world. The trends that drive this are well known: environmental challenges, population growth, urbanization and increasing demands for mobility.
With 6B€ of Sales and around 30,000 employees based in over 60 countries, Alstom develop & market the most complete range of systems, equipment and services offered today in the railway sector. Today we offer our customers solutions that feature a seamless blend of diverse technologies, ensuring optimal interfaces, along with flexible implementation and real synergy in innovation.

Context

Prognostics and health management (PHM) consists of assessing the current and predicting the future health state of a system to optimize its maintenance. PHM is performed from the embedded extraction of relevant features named health indicators (HIs), from which symptoms are generated. Then, some processing algorithms elaborate diagnosis and prognosis that are used to maximize the system’s availability and reliability by optimizing the maintenance schedule.

Alstom Digital Mobility is launching various other innovative programs which will require data science skills , such as ‘intelligent traffic management”,  driver assistance, energy management, advanced field reliability analysis, etc.

Job description

Internship (Stage) : - State-of-the-art survey in advanced prognostics methods

 Tremendous progress has taken place over the last 2 decades in machine learning and modern statistical analysis. Along with the concomitant evolution in computing power – including cloud computing-, this affords the opportunity to perform data analyses which are more powerful and more far reaching than heretofore.

Although there are many applications for data science, one of particular interest is prognostics for  predictive maintenance. Data driven methods and model-based approaches are complementary and can be combined.

In that context, your activities will be the following.

·  State-of-the-art survey in advanced prognostics methods
·  Perform a survey of data driven methods, including the recent advances such as  the type of neural network approach designated by the term ‘ deep learning’.
·  Examine potential application to some of the subsystems studied in Alstom’s Predictive Maintenance R&D program, such as point machines, doors, and others.
·   Conclude with practical and theoretical recommendations.

Candidate profile

Student in final year of engineering school with strong mathematical emphasis, or applied mathematics or statistics.

Candidate skills

·  Familarity with modern statistics and data science, as well as  stochastic processes.
·  Data analysis software: Matlab, Excel,  R a plus.
·  SQL
·  Technical English;
·  Autonomy and initiative.  Team spirit.

The candidate  will spend 6 months in the Predictive Maintenance Center of  Excellence