Senior Applied Scientist, Amazon Audiences - ADSP

Join Amazon Advertising's targeting program as a Senior Applied Scientist where you'll help shape how advertisers reach relevant customers through advanced ML and statistical techniques. Our mission is to ensure targeted ads meet high relevancy standards that benefit both customers and advertisers.

Working from either our Edinburgh development center or London head office, you'll be part of an agile, fast-paced team of scientists and software engineers. You'll have end-to-end ownership of the scientific process - from ideation and business analysis to research and model deployment. From your first day, you'll collaborate with experienced scientists, engineers, and designers who are passionate about their work.

Key job responsibilities
This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. You will be responsible for driving end-to-end scientific processes from ideation to deployment, conducting business analysis and scientific research, and developing advanced ML models. You will apply sophisticated ML and statistical techniques to optimize advertising systems and ensure targeted ads meet high relevancy standards. Working in an agile environment, you'll collaborate closely with scientists, engineers, and designers to develop innovative advertising solutions. Your responsibilities include helping advertisers optimize their targeting strategies, improving customer experience through relevant ad delivery, and contributing to the growth of Amazon's advertising business. We place a high emphasis on team spirit and collaboration, while providing the required support needed for succeeding in the role.

A day in the life
A typical day would involve collaborating with a diverse team of scientists, engineers, and designers in either the Edinburgh development center or London head office. You might start your day participating in agile team meetings to discuss ongoing projects and align on priorities. Throughout the day, you would be engaged in various aspects of the scientific process, including:

* Analyzing business problems and conducting scientific research to develop solutions for advertising targeting
* Collaborating with team members to review code, discuss technical approaches, and refine solutions
* Participating in technical discussions to evaluate the effectiveness of targeting strategies

About the team
Amazon Audiences is a specialized organization within Amazon Advertising that focuses on developing advanced targeting solutions. The team consists of several specialized groups working on different aspects of advertising technology. These include building scalable targeting services that work without relying on user IDs, developing contextual targeting capabilities that match ads with relevant content, enabling keyword-based targeting for advertisers, providing technology-based targeting options for different devices and platforms, and creating tools that help advertisers define and understand their target audiences using Amazon's retail and digital signals. The team also includes specialists who focus on search engine optimization and keyword research to help advertisers improve their visibility and reach. Throughout all these functions, the team maintains a strong commitment to preserving customer privacy while helping advertisers effectively reach their target audiences.

BASIC QUALIFICATIONS

- Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience in building machine learning models for business application
- Experience in applied research
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

PREFERRED QUALIFICATIONS

- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
- PhD