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Join a forward-thinking company as a Sr. Applied Scientist in Berlin, where you'll leverage your expertise in machine learning to tackle complex challenges and drive innovation. This role offers the opportunity to work with cutting-edge technologies, collaborating with diverse teams to create impactful solutions that enhance service reliability and user experience. You'll be at the forefront of developing scalable models and data processing pipelines, ensuring that your contributions resonate across millions of users globally. If you're passionate about applying your scientific knowledge in a practical setting and eager to grow as a technical leader, this position is perfect for you.
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Amazon Development Center Germany GmbH
Berlin, Germany
Other
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Yes
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537992e56c70
1
30.04.2025
14.06.2025
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Our team builds data-driven automation capabilities to support critical service operations in Retail and IT with global impact. Automation improves the operations and availability of consumer services with a positive impact on experience of millions of users every year. Our work increases service operation resilience, automates incident response process and enables us to act ahead of service disruptions, while simplifying system and information complexity. We invent practical approaches within application areas such as anomaly detection, time series analysis, classification, causal inference, and text mining, and we apply the latest and most sound techniques of agentic workflows with Large Language Models (LLMs), probabilistic modelling, estimation and deep neural networks. Working with us offers exciting challenges where you will grow as an applied scientist and technical leader, combining your scientific and engineering skills to solve complex machine learning problems together with our tech teams around the world.
Key job responsibilities
As a Sr. Applied Scientist of the Engine AICE team, you have the important role of mapping business challenges to high-impact solutions in areas where the business problem or opportunity may not yet be defined. You turn theoretically sound methods into practically applicable models designed for processing massive volumes of data in large-scale environments. You define business relevant solutions implemented as end-to-end machine learning functions and data processing pipelines that integrate with our partners production systems. In a fast-paced innovation environment, you advise and work closely with our Applied Scientists, Machine Learning Engineers, Software Development Engineers, and partner teams to design machine learning models and experiments at scale. You are recognized for your expertise in all aspects of the practical machine learning development cycle, encompassing sound use of data pre-processing techniques, analysis, modelling, and validation methods. You take lead of the scientific and technical work in cross-team collaborations.
A day in the life
Almost everyday offers new challenges and opportunities for growth. Where one day will offer deep dives into technical requirements and applicability of state-of-the-art models to automated detection and root cause analysis of service disruptions, the next day may be focused on experimental design and implementation of model evaluations. Later in the week, you may sort technical and business requirements with our partners to help them enrich their products with our models. On some days or weeks, you may dive deep into the performance of deployed models to decide and communicate the next steps of model maintenance to our partners.
About the team
We work back to back to address the technical challenges of automation across a variety of products, software, and systems. Our scientists and machine learning engineers work in synergy to solve hard problems and enrich each other's skills. Together, we are a diverse team of specialists that bring the potential of practical machine learning to the max with impact on millions of Amazon customers.
BASIC QUALIFICATIONS
- PhD degree in Computer Science or related fieldPREFERRED QUALIFICATIONS
- Experience in any of the following or similar areas: anomaly detection, time series analysis, LLM-agents, correlation analysis, causality modelling, graph modelling, probabilistic modelling, nlp, text mining.