Amazon
Applied Scientist II, Automated Inventory Management
Amazon, Bellevue, Washington, us, 98009
Applied Scientist II, Automated Inventory Management
Amazon’s Automated Inventory Management (AIM) team is looking for passionate, hard-working, and talented individuals to join our fast-paced, stimulating environment to help invent the future of business ownership with Technology, and to translate big data into actionable insights.
The AIM team is part of the Supply Chain Optimization Technology (SCOT) Team within the Operations Organization.As an Applied Scientist on the AIM team, you will design quantitative systems, prediction models and solve real-world problems using the latest machine learning techniques. You will also work with a team of Product Managers, Business Intelligence Engineers, and Software Engineers to research and build solutions to provide insights to business leaders at the most senior levels throughout the company.Key job responsibilities:Implement statistical and machine learning methods to solve complex business problems.Research new ways to improve predictive and explanatory models.Directly contribute to the design and development of automated prediction systems and ML infrastructure.Build models that can detect supply chain defects and explain variance to the optimal state.Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team.Minimum qualifications:2+ years of building models for business application experience.PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience.Experience programming in Java, C++, Python or related language.Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.Experience using Unix/Linux.Experience in professional software development.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit
https://www.amazon.jobs/en/disability/us .Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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Amazon’s Automated Inventory Management (AIM) team is looking for passionate, hard-working, and talented individuals to join our fast-paced, stimulating environment to help invent the future of business ownership with Technology, and to translate big data into actionable insights.
The AIM team is part of the Supply Chain Optimization Technology (SCOT) Team within the Operations Organization.As an Applied Scientist on the AIM team, you will design quantitative systems, prediction models and solve real-world problems using the latest machine learning techniques. You will also work with a team of Product Managers, Business Intelligence Engineers, and Software Engineers to research and build solutions to provide insights to business leaders at the most senior levels throughout the company.Key job responsibilities:Implement statistical and machine learning methods to solve complex business problems.Research new ways to improve predictive and explanatory models.Directly contribute to the design and development of automated prediction systems and ML infrastructure.Build models that can detect supply chain defects and explain variance to the optimal state.Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team.Minimum qualifications:2+ years of building models for business application experience.PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience.Experience programming in Java, C++, Python or related language.Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.Experience using Unix/Linux.Experience in professional software development.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit
https://www.amazon.jobs/en/disability/us .Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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