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.
- PhD degree in Computer Science or related field
- Several years experience building machine learning models for business applications using Python, Java, C++ or related language.
- Documented expertise in machine learning/artificial intelligence: data processing, neural networks, deep learning, estimators, regression, information theory, optimization, statistical analysis, signal processing, graph mining, causality analysis.
- Experience with patents or scientific publications at peer-reviewed conferences or journals and generally excellent writing and communication skills.
- 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.
- Experience in data-driven and automated fault/incident management and service reliability systems at scale.
- Experience with machine learning frameworks, distributed storage systems, or data processing frameworks, and data visualization tools.
- Experience in designing and developing large, scalable production systems and architectures.
- Project leader and/or team lead experience.
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