Applied Scientist - Delivery Experience, Delivery Experience AI Team at Amazon
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machine learning
reinforcement learning
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Why this job is awesome?
- This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.
- MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.
- We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.
Do you want to join an innovative team of scientists and engineers who use advanced machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site?
Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?
Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?
Do you like to innovate and simplify?
If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.
Major responsibilities:
- Work closely with business teams to identify business and science opportunities for providing the best delivery experience on all Amazon-owned sites
- Analyze a large amount of historical data to detect patterns, analyze trends and identify correlations and causalities
- Innovate, design, develop and evaluate machine learning models to solve business problems and improve customer experience
- Work closely with software engineering teams to establish automated, efficient and scalable processes for data collection, retention and analysis as well as model development, validation, deployment and maintenance
Key job responsibilities
Key job responsibilities:
- Work with business owners to understand business goals, requirements and use cases
- Map abstract business goals and requirements to well-defined science projects with executable plans and milestones
- Work with engineers to collect data in different formats from various sources
- Use existing platforms and tools or write codes to perform statistical data analysis
- Create machine learning prototypes to evaluate feasibilities of approaches, gain insights and drive decisions
- Design and apply (mathematical formulation and coding required) machine learning models (supervised, unsupervised, reinforcement learning, casual inference and uplift, etc.) to solve business problems
- Evaluate, analyze and interpret model results, and further improve model performance
- Work with software engineers to deploy and scale science work on production
- Communicate with stakeholders on project progress and results both orally and in writing
- Share findings and learnings with teams and internal science community
- Mentor junior scientists to help them grow
- This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.
- MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.
- We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.
Do you want to join an innovative team of scientists and engineers who use advanced machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site?
Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?
Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?
Do you like to innovate and simplify?
If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.
Major responsibilities:
- Work closely with business teams to identify business and science opportunities for providing the best delivery experience on all Amazon-owned sites
- Analyze a large amount of historical data to detect patterns, analyze trends and identify correlations and causalities
- Innovate, design, develop and evaluate machine learning models to solve business problems and improve customer experience
- Work closely with software engineering teams to establish automated, efficient and scalable processes for data collection, retention and analysis as well as model development, validation, deployment and maintenance
Key job responsibilities
Key job responsibilities:
- Work with business owners to understand business goals, requirements and use cases
- Map abstract business goals and requirements to well-defined science projects with executable plans and milestones
- Work with engineers to collect data in different formats from various sources
- Use existing platforms and tools or write codes to perform statistical data analysis
- Create machine learning prototypes to evaluate feasibilities of approaches, gain insights and drive decisions
- Design and apply (mathematical formulation and coding required) machine learning models (supervised, unsupervised, reinforcement learning, casual inference and uplift, etc.) to solve business problems
- Evaluate, analyze and interpret model results, and further improve model performance
- Work with software engineers to deploy and scale science work on production
- Communicate with stakeholders on project progress and results both orally and in writing
- Share findings and learnings with teams and internal science community
- Mentor junior scientists to help them grow