Principal Applied Scientist, Personalization Team

Amazon.com, Inc

Principal Applied Scientist, Personalization Team

Salary Not Specified

Amazon.com, Inc, Edinburgh

  • Full time
  • Permanent
  • Onsite working

Posted 1 week ago, 2 Jul | Get your application in now before you miss out!

Closing date: Closing date not specified

job Ref: 750dc2685ea042c48f95047fcd26447e

Full Job Description

We're looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization.

You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization.

Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon's vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide.

Key job responsibilities
Develop machine learning algorithms for high-scale recommendations problem

Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement.

Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency.

Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.