Applied Scientist, Prime Video Commerce Insights

Description

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 any time 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.

Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video in every market and on every device. Our science team replaces static business rules with ML‑driven decisions that personalise the entire commerce journey, from discovery through to checkout and beyond. We operate at scale across hundreds of millions of customers, and we are now expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers.

We are looking for an Applied Scientist to join the Prime Video Commerce Insights team who will work on the latest research and machine learning to build scalable personalisation solutions. You will develop and deploy customer‑facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a hands‑on role working with a high‑performing and high‑visibility multidisciplinary group of engineers and scientists in the London office, focused on improving the customer experience for Prime Video and the wider Amazon organisation.

You will contribute to the design of machine learning models that scale to large quantities of data and serve low‑latency recommendations to all customers worldwide. You will embody scientific rigor in designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science and engineering team that embodies the customer obsession principle by developing recommendation and decision systems that raise the profile of Prime Video Commerce as a global leader in machine learning and personalisation.

Successful candidates will have strong technical ability, a focus on customers by applying a customer‑first approach, and excellent teamwork and communication skills. The position offers exceptional opportunities for every candidate to grow their technical and non‑technical skills.

• Research, design, and implement recommendation systems that personalise across different customer experience touch points.
• Collaborate with engineers to deploy and integrate successful model experiment results into large‑scale, complex Amazon production systems with low latency.
• Provide machine learning thought leadership to both technical and business leaders, with the ability to think strategically about business, product, and technical challenges.
• Be a subject matter expert in reinforcement learning approaches for the team and actively contribute to the science roadmap.
• Define the science roadmap and research agenda that aligns with the organisation's priorities and production constraints.
• Work with technical product managers to work backwards from what's important to customers and deliver machine‑backed solutions.
• Report and share results with the team and wider scientific community by authoring documents that are both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.

You…