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Applied Scientist (Machine Learning)

Job ID: 1826399 | Amazon Data Services, Inc.

DESCRIPTION

Job summary
The AWS Infrastructure Planning group is responsible for planning and coordinating a complex, multi-tier supply chain that delivers capacity for all AWS services. We are responsible for ensuring that the AWS cloud remains elastic for its customers by taking care of all of the back-end complexity, enabling our infrastructure to stay ahead of our rapid growth.

As part of the Applied Research team, you’ll partner closely with other software development engineers and product managers with a clear path to business impact. Working alongside fellow applied scientists, you’ll leverage your expertise to develop new machine learning techniques and develop statistical models for demand forecasting at different levels of aggregation, lead time and supply predictions, order promising, supply chain risk detection and simulation of complex systems.

Successful candidates will have a deep knowledge of statistical and machine learning methods, the ability to map models into scalable production systems, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the ability to take an iterative approach to tackle ambiguous long-term problems.


Key job responsibilities
· Understand business requirements and existing challenges and map them to the right scientific solution.
· Research, prototype, experiment, implement and launch machine learning models to solve problems that matter to our customers.
· Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans
· Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams.
· Influence the organization's long-term roadmap and resourcing, onboard new technologies onto our team's toolbox
· Develop the right set of metrics to evaluate efficiency/accuracy of the algorithms.
· Mentor and develop the scientist community across the organization.


About the team
The AWS Infrastructure Supply Chain Technology Applied Research team drives technological advances and develops machine learning, optimization and simulation products to address complex challenges in the AWS Infrastructure supply chain.

We sit at the intersection of science and engineering, and innovate on behalf of customers to solve ambiguous problems that haven’t quite been fully articulated or even anticipated.

BASIC QUALIFICATIONS

· PhD degree in Engineering, Computer Science, Applied Mathematics, Statistics, Operations Research and related fields or MS degree + 4 years of experience
· 1+ years hands-on experience developing software in Python, Java, C#, C++ or other object-oriented programming languages
· 2+ years of hands-on experience building machine learning models for business applications
· Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning

PREFERRED QUALIFICATIONS

· PhD degree in Engineering, Computer Science, Applied Mathematics, Statistics, Operations Research or related fields
· 4+ years of hands-on experience in building machine learning systems and integrating AI/ML into large-scale production applications
· Familiarity deploying solutions to AWS or cloud services and experience with AWS services such as AWS Lambda, SageMaker, Amazon S3 and Amazon Elastic Container Service is considered a plus
· Publications at peer-reviewed conferences or journals
· Demonstrated industry leadership in your area of specialization



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.