- MSc in Computer Science, Mathematics, Statistics, Engineering, or equivalent experience;
- 4+ year experience in algorithm design, engineering and implementation for very-large scale applications to solve real problems;
- Proficient using R, Python, or other equivalent statistics and machine learning tools;
- Experience with MySQL/PostgreSQL/Redshift;
- Knowledge of AWS Infrastructure;
- Strong interpersonal and communication skills.;
- Experienced in computer science fundamentals such as object-oriented design, data structures and algorithm design.
Are you a talented and inventive scientist with strong passion about using theoretical data science model in an applied environment? Would you like to play a key role within EU RME Predictive Analytics team? Our mission is to provide EU RME with the technical expertise to support World Class Maintenance and Spare Parts Programs. As Data Scientists you will be working with large distributed systems of data and providing Machine Learning (ML) and Predictive Modeling expertise for over 2000 maintenance engineers, managers and administrators by supporting the entire network managed by EU RME, which may include non-EU locations (such as Singapore, Australia and Japan). You will connect with world leaders in your field and you will be tackling ML challenges by carrying out a systematic review of existing solutions. The appropriate choice of the methods and their deployment into effective tools will be the key for the success in this role.
The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices.
- Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement;
- Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares;
- Provide technical expertise to support the development of predictive and optimization models used to reduce energy consumption and promote sustainability;
- Collaborate with EU RME internal and external stakeholders and have a cross-team impact;
- Create and share with audiences of varying levels technical papers and presentation.
- Experienced in applying theoretical models in an applied environment;
- Experienced in writing academic-styled papers for presenting both the methodologies used and results for data science projects;
- Basic skills in probabilistic modeling and reliability methods.