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Applied Science Manager at Amazon Development Center DEU

Applied Science Manager at Amazon Development Center DEU

Deadline: ongoing
Open to: candidates with a Master’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field; or equivalent combination of technical education and work experience
Venue: Berlin, Germany

Description

Amazon’s Intelligent Cloud Control Group in Berlin is looking for an Applied Science Manager to lead the research and development of large-scale machine learning implementations that will revolutionize the way Amazon operates the tens of thousands of software services and subcomponents across our retail businesses. With an ever-growing number of fleets, developers, customers, products, marketplaces, sellers, and businesses, the Amazon service graph is one of the largest and most complex tech ecosystems in the world. They are building an Intelligent Cloud Control system that enables Amazon businesses (Retail, Amazon Video, Kindle, and more) to accelerate innovation in the cloud.

As an Applied Science Manager in the Intelligent Cloud Control team, you’ll leverage your own skills and those of your team of machine learning engineers, data scientists, and applied scientists to develop and evaluate machine learning models using extremely large datasets such as the orders, website traffic, telemetry, and logs from every host at Amazon. Our datasets extend into the multi-exabyte range, and our science products are of critical importance to the retail businesses of Amazon. You will own researching, developing, prioritizing, and releasing both prototypes and reliable automated production workflow for the model. You will collaborate with other managers and leaders to improve the Amazon retail customer experience.

Their responsibility is to maximize the availability and contribute to the better efficiency of Amazon’s retail experience, so our opportunities are endless. From natural language processing and information extraction of operational issues to unsupervised multi-variate anomaly detection to discover nodes of linked sub-system behavior, the insights and opportunities to discover and remediate customer-impacting issues are profound and our solutions worthy of publication.

They are looking for engineers capable of using machine learning and other techniques to design, evaluate, and implement state-of-the-art solutions for never-before-solved problems.

Eligibility

BASIC QUALIFICATIONS

· A Master’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field; or equivalent combination of technical education and work experience.
· 4+ years of experience in Applied Machine Learning, Statistics, or a closely-related field.
· 1+ years of experience managing a software engineering or machine learning science team.
· Must have delivered features for at least one large-scale production system.

PREFERRED QUALIFICATIONS

· PhD in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field; or equivalent combination of technical education and work experience.
· 6+ years of experience in Applied Machine Learning, Statistics, or a closely-related field.
· 2+ years of experience managing a software engineering or machine learning science team.
· Excellent written and verbal communication skills with the ability to present complex technical information in a clear and concise manner to a variety of audiences.
· A proven track record of hiring and developing software engineers or machine learning scientists.
· A strong sense of curiosity and willingness to learn quickly, building knowledge and skills that this role requires.
· A deep understanding of the software development lifecycle, and a track record of shipping software on time.
· Experience with the Scrum methodology (or similar alternatives) for agile software development.
· Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
· Deep hands-on technical expertise in cloud-based distributed software design and development, especially utilizing AWS services.
· Knowledge of machine learning approaches and algorithms, and experience building complex highly-scalable systems that involve predictive models or applications of machine learning.
· Ability to handle multiple competing priorities in a fast-paced environment.

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