Job Description
Description
As a Data Science Manager at AFT Inventory, you own building and science teams that meet the needs of the business, advance data science technology, and work well with non-science stakeholders and scientists. Teams may consists of but aren’t limited to Scientists, Business Intelligence Engineers, Data Engineers, Software Development Engineers and Technical Program Managers. Besides setting long-term direction, you own the tactical aspects of improving the customer experience, developing a customer roadmap, and driving the development of new models and algorithms that have an impact on millions of customers.
You draw from a broad data science expertise to mentor Scientists and Business Intelligence Engineers; following a rigorous scientific methodology, while providing leadership on complex technology issues. You guide your team in latest methods in big data processing, data science literature, experimentation and careful consideration of modeling decisions. We expect you to have breadth of data science knowledge, and experience with the management of technical teams. Your role includes hiring top talent, providing feedback, promoting, overall allocation strategy and meeting frequency strategy.
You will help guide the science team to piece together data from multiple sources, models, and
infrastructure into a cohesive end-to-end solution (Focus: statistical, analytical, data management and
data science). You will have the opportunity to work across a wide variety of areas while acting as a link
between business-facing communication and technical data manipulation.
About the team
AFT Inventory team is at the heart of Amazon e-commerce supply chain. The team manages the creation and movement of inventory through Amazon Fulfilment centers, creating the best in class selection for our customers while ensuring the best vendor and seller experience
Basic Qualifications
- 5+ years of building quantitative solutions as a scientist or science manager experience
- 2+ years of scientists or machine learning engineers management experience
- 5+ years of applying statistical models for large-scale application and building automated analytical systems experience
- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Knowledge of Python or R or other scripting language
Preferred Qualifications
- Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection)
- Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
As a Data Science Manager at AFT Inventory, you own building and science teams that meet the needs of the business, advance data science technology, and work well with non-science stakeholders and scientists. Teams may consists of but aren’t limited to Scientists, Business Intelligence Engineers, Data Engineers, Software Development Engineers and Technical Program Managers. Besides setting long-term direction, you own the tactical aspects of improving the customer experience, developing a customer roadmap, and driving the development of new models and algorithms that have an impact on millions of customers.
You draw from a broad data science expertise to mentor Scientists and Business Intelligence Engineers; following a rigorous scientific methodology, while providing leadership on complex technology issues. You guide your team in latest methods in big data processing, data science literature, experimentation and careful consideration of modeling decisions. We expect you to have breadth of data science knowledge, and experience with the management of technical teams. Your role includes hiring top talent, providing feedback, promoting, overall allocation strategy and meeting frequency strategy.
You will help guide the science team to piece together data from multiple sources, models, and
infrastructure into a cohesive end-to-end solution (Focus: statistical, analytical, data management and
data science). You will have the opportunity to work across a wide variety of areas while acting as a link
between business-facing communication and technical data manipulation.
About the team
AFT Inventory team is at the heart of Amazon e-commerce supply chain. The team manages the creation and movement of inventory through Amazon Fulfilment centers, creating the best in class selection for our customers while ensuring the best vendor and seller experience
Basic Qualifications
- 5+ years of building quantitative solutions as a scientist or science manager experience
- 2+ years of scientists or machine learning engineers management experience
- 5+ years of applying statistical models for large-scale application and building automated analytical systems experience
- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Knowledge of Python or R or other scripting language
Preferred Qualifications
- Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection)
- Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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