Applied Scientist I, Customer Delivery Excellence Science
Amazon
Bellevue, WA, US
Not specified; not specified; not specified
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Machine learning model implementation
Predictive modeling for delivery times
Statistical analysis of logistics data
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Amazon is seeking an Applied Scientist I for their Customer Delivery Experience Science Team in Bellevue, WA, to leverage data-driven modeling and machine learning techniques to enhance global logistics and delivery experiences. The role emphasizes collaboration with logistics operations teams to build predictive models while fostering a culture of continuous learning and professional growth.
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Job Summary
Join Amazon's Customer Delivery Experience Science Team to improve global logistics through advanced machine learning and data-driven modeling.
You will build and validate predictive models for delivery time estimation using historical data, weather patterns, and traffic information.
The role offers a structured mentorship program, regular 1:1s, and access to Amazon's ML University for continued professional development.
Matching Summary
Match Score: 75
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Amazon is seeking an Applied Scientist I for their Customer Delivery Experience Science Team in Bellevue, WA, to leverage data-driven modeling and machine learning techniques to enhance global logistics and delivery experiences. The role emphasizes collaboration with logistics operations teams to build predictive models while fostering a culture of continuous learning and professional growth.
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Salary
Not specified; Not specified; Not specified
Skills & Requirements
Must-have
Machine Learning model implementation
Predictive modeling for delivery times
Statistical analysis of logistics data
Python or ML framework proficiency
Collaboration with operations teams
Nice-to-have
Mentorship and professional growth support
Diverse team perspectives valued
Access to internal science forums
Conference attendance opportunities
Key Requirements
Experience with established ML frameworks
Ability to translate business requirements into modeling approaches
Strong documentation skills for methodologies and limitations