Applied Scientist, Anomaly Detection & Insights
London, England, GBR
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Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?
Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.
You will have the opportunity to work with a variety of leading technologies, leveraging AWS. You will help shape the future of Amazon’s video platform by investigating new approaches to provide the best video user experience for customers. This will be balanced with sound engineering practices leveraging tools, data and machine learning. Your goal is to build the best video playback application for the world's most customer-centric company.
We are looking for an Applied Scientist to join Prime Video. In this role you will leverage your strong background in Machine Learning to help build the next generation of Time Series Anomaly Detection systems. You will apply your deep knowledge of machine learning to concrete problems that have broad cross-organizational, global, and technology impact. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. The ideal candidate will have experience with machine learning models, and additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.
Key job responsibilities
You will work as part of one of our Agile teams, launching and growing new initiatives for Amazon's global business. As an applied scientist, you will be involved in every aspect of the process - from research, idea generation, and data analysis through to development and deployment across a variety of technologies - giving you true ownership on the future of our products. Together we take on hard technical problems, and build systems which meet high standards of performance and operate at massive scale.
Amazon is a place where we use science and engineering to solve problems. We're looking for scientists and engineers who can delight customers by continually learning and inventing. From day one, you'll be working with experienced engineers, machine learning experts and other applied scientists who love what they do.
A day in the life
You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions.
We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers.
About the team
The team is based in Amazon's engineering centre in London and consists of engineers and applied scientists with a variety of backgrounds, from seasoned Amazonian to newly hired; with industry experience and straight from college; a mix of ML experts, service experts, and generalists, but all of us learning and growing. We work closely with other Prime Video engineering teams, including teams based on the US west coast and India as well as in London.
- Experience programming in Java, C++, Python or related language
- Master's in Computer Science, Mathematics, Machine Learning, or related quantitative field
- Masters or PhD degree in Computer Science, Machine Learning, Applied Mathematics, Operations Research or a related field, or equivalent work experience
- Experience in designing analytic and/or algorithmic solutions to business or operational problems
- Experience implementing algorithms, tailored to particular business needs and tested on large data sets
- Excellent communication skills with both technical and non-technical audiences
- Ability to work independently and as part of a diverse team.
- Experience with time series forecasting techniques and behavioural clustering techniques
- Experience programming in Python, R or Matlab or other statistical/machine learning software languages or related language
- Experience of building machine learning models for business application
- Theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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