Description
- MS in Operations Research, 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 and experience of using commercial software such as CPLEX and/or XPRESS
- Strong interpersonal and communication skills.
- Experienced in computer science fundamentals such as object-oriented design, data structures and algorithm design
- PhD in Operations Research, 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
- Experience in proposing and developing heuristic and Metaheuristics for large scale problems.
- Experience in solving a variety of academic problems such as VRP, Unit Commitment, Inventory Management.
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.
The Decision, Science and Technology (DST) team part of the global Reliability Maintenance Engineering (RME) is looking for a Senior Operations Research Scientist interested in solving challenging optimization problems in the maintenance space.
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 a OR 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 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 a Senior OR 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 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.
- Proficient using R, Python, or other equivalent statistics and machine learning tools
- Experience with MySQL/PostgreSQL/Redshift
- Knowledge and experience of using commercial software such as CPLEX and/or XPRESS
- Strong interpersonal and communication skills.
- Experienced in computer science fundamentals such as object-oriented design, data structures and algorithm design
- PhD in Operations Research, 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
- Experience in proposing and developing heuristic and Metaheuristics for large scale problems.
- Experience in solving a variety of academic problems such as VRP, Unit Commitment, Inventory Management.
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.
The Decision, Science and Technology (DST) team part of the global Reliability Maintenance Engineering (RME) is looking for a Senior Operations Research Scientist interested in solving challenging optimization problems in the maintenance space.
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 a OR 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 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 a Senior OR 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 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.
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