Description

　　Are you a talented and inventive scientist with strong passion about AI applications? Would you like to develop machine learning and optimization tools by playing a key role in the Decision Science and Technology (DST) team within the Global RME Central organization? Our mission is to leverage the use of data, science, and technology to improve the efficiency of RME maintenance activities, reduce costs, increase safety and promote sustainability while creating frictionless customer experiences.

　　As Applied Scientist in DST you will be focused on leading the design and development of innovative approaches and solutions by leading technical work supporting RME’s Predictive Maintenance (PdM) and Spare Parts (SP) programs.

　　You will connect with world leaders in your field and you will be tackling customer's natural language challenges by carrying out a systematic review of existing solutions. The appropriate choice of AI 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.

　　Key job responsibilities

　　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

　　Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime

　　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

　　A day in the life

　　As Applied Scientist in DST you will be focused on leading the design and development of innovative approaches and solutions by leading technical work supporting RME’s Predictive Maintenance (PdM) and Spare Parts (SP) programs.

　　You will connect with world leaders in your field and you will be tackling customer's natural language challenges by carrying out a systematic review of existing solutions. The appropriate choice of AI methods and their deployment into effective tools will be the key for the success in this role.

　　About the team

　　Our mission is to leverage the use of data, science, and technology to improve the efficiency of RME maintenance activities, reduce costs, increase safety and promote sustainability while creating frictionless customer experiences.

　　We are open to hiring candidates to work out of one of the following locations:

　　Luxembourg, LUX

　　Basic Qualifications

　　MS in Data Science, Machine Learning, Statistics, Computer Science, Applied Math or equivalent highly technical field

　　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

　　Preferred Qualifications

　　PhD in Data Science, Machine Learning, Statistics, Computer Science, Applied Math or equivalent highly technical field

　　Working experience in applied science and/or machine learning using models and methods such as neural networks, random forests, SVMs or Bayesian classification

　　Experience 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

　　Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.