Our mission is to build the automated intelligence supporting critical service operations at global scale. The Intelligent Cloud Control Machine Learning (ICCML) team works to automate complex large-scale operations of Amazon’s consumer services by developing data-driven, scalable, and seamless solutions available to customers and ICC partners. We employ machine learning to reduce system and information complexity while improving service reliability. 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 probabilistic modelling, estimation, deep neural networks, and natural language processing (NLP). 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.
As an Applied Scientist of the ICCML team, you will have the important role of mapping business problems to high-impact solutions. You will turn theoretically sound methods into practically applicable models designed for processing massive volumes of data in large-scale environments. You will 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 will work closely with our Applied Scientists, Machine Learning Engineers, and partners to design machine learning models and experiments at scale. You dive deep into all aspects of the practical machine learning development cycle, encompassing sound use of data pre-processing techniques, analysis, modelling, and validation methods. You master the complex theory under the hood of machine learning and you keep up to date with the latest scientific development in information processing, modelling, and learning methods. You take lead of the scientific and technical work in cross-team collaborations with the ultimate objective of creating a delightful experience for our customers using our services.
· MSc or PhD degree in Computer Science or related field.
· For PhD: At least 4 years of applied research experience working with industrial applications.
· For MSc: At least 6 years of applied research experience working with industrial applications.
· Knowledge of Computer Science fundamentals such as object-oriented design, algorithm design, data structures, problem solving and complexity analysis.
· Documented expertise in machine learning/artificial intelligence: data processing, neural networks, deep learning, estimators, regression, information theory, optimization, statistical analysis, signal processing.
· Demonstrated capability to handle challenges with vague or abstract problem definition.
· Project leader and/or team lead experience.
· Scientific publication experience in conferences and/or journals and generally excellent writing and communication skills in English.
· Technical hands-on experience in general-purpose programming languages such as Scala, Python, Java, or C++.
· Experience in any of the following or similar areas: anomaly detection, time series analysis, 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.
· Experience in AWS Lambda, AWS SageMaker, Jupyter Notebook.