Applied Scientist

Who you are

• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience in building machine learning models for business application
• Professional non-internship experience in a relevant field (CS, CE, ML)
• Experience implementing algorithms using both toolkits and self-developed code
• Have publications at top-tier peer-reviewed conferences or journals
• Master's degree in a relevant field, or equivalent qualification/experience

What the job involves

• We are looking for an Applied Scientist to join the Prime Video Commerce Insights team in London
• 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 delivery focused role within a high-visibility multidisciplinary group of engineers and scientists, focused on improving the customer experience for Prime Video
• Research, design, and implement machine learning approaches (e.g. reinforcement learning and recommendation systems) that personalise across different customer touch points
• Collaborate with engineers to deploy and integrate successful experiment results into large-scale, complex Amazon production systems with low latency
• Design and execute rigorous experiments to demonstrate the technical efficacy and business value of your methods
• Act as a subject-matter expert and help define the science roadmap and research agenda in line with organisational priorities and production constraints
• Provide machine learning thought leadership to technical and business leaders, thinking strategically about business, product, and technical challenges
• Work with technical product managers to work backwards from what matters to customers and deliver ML-backed solutions
• Share results with the team and wider scientific community through documents that are both statistically rigorous and compellingly relevant
• A day in the life:
• You will be a research leader and innovator within the Commerce Insights organisation. You will collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines, balancing competing objectives, and positively impacting hundreds of millions of customers worldwide
• The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer-experience consequences. Your research will ship to production and move metrics that matter