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Applied Scientist

Job ID: 2633625 | Amazon Development Centre Italy SRL

DESCRIPTION

The Artificial General Intelligent team (AGI) seeks a Applied Scientist with a strong background in machine and deep learning to spearhead the advancement and deployment of cutting-edge ML systems. As part of this team, you will collaborate with talented peers to create scalable solutions for an innovative conversational assistant, aiming to revolutionize user experiences for millions of Alexa customers. Creating reliable, scalable and high performance products requires exceptional technical expertise, and a sound understanding of the fundamentals of Machine Learning, NLP and Problem solving. This role requires working closely with business, engineering and other scientists within the team and across Amazon to raise the bar in operational excellence, improving tools and automating workflows. You will lead high visibility and high impact programs collaborating with various teams across Amazon.
The candidate is self-motivated, thrives in ambiguous and fast-paced environments, possess the drive to tackle complex challenges, and excel at swiftly delivering impactful solutions while iterating based on user feedback. Join us in our mission to redefine industry standards and provide unparalleled experiences for our customers.


Key job responsibilities
* Analyze, understand, and model customer behavior and the customer experience based on large scale data
* Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints
* Create, innovate and deliver deep learning, policy-based learning, and/or machine learning based algorithms to deliver customer-impacting results
* Build and deploy automated model training and evaluation pipelines
* Perform model/data analysis and monitor metrics through online A/B testing
* Research and implement novel machine learning and deep learning algorithms and models.

We are open to hiring candidates to work out of one of the following locations:

Turin, ITA

BASIC QUALIFICATIONS

* PhD, or a Master's degree and experience in Computer Science, Computer Engineering, Machine Learning or related field
* Experience building machine learning models or developing algorithms for business application
* Bar raising knowledge of programming languages such as Python or Java, with a strong focus on machine learning frameworks
* Experience in any of the following areas: algorithms and data structures, algorithms, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
* Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
* Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting.
* Fluence in written and spoken English

PREFERRED QUALIFICATIONS

* 5+ years of building machine learning models or developing algorithms for business application experience
* Have publications at top-tier peer-reviewed conferences or journals
* Track record of diving into data to discover hidden patterns and conducting error/deviation analysis
* Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
* Exceptional level of organization and strong attention to detail
* Comfortable working in a fast paced, highly collaborative, dynamic work environment

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.