We are the Intelligent Cloud Control Machine Learning (ICCML) team, and our mission is to build the automated intelligence at Amazon that supports critical service operations at global scale. We automate complex large-scale operations of Amazon’s consumer services by developing data-driven, scalable, and seamless solutions available to customers and partners. We employ data science and machine learning to reduce system and information complexity while improving service reliability. We invent practical approaches within 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 a data scientist and technical leader, combining your data science and engineering skills to solve complex information processing problems together with our tech teams around the world.
As a Data 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 based on analytics and data processing at scale, supporting and contributing to the development of end-to-end machine learning solutions and data processing pipelines that integrate with our partners’ production systems. In a fast-paced innovation environment, you will work closely with our Data Scientists, Applied Scientists, Machine Learning Engineers, and partners, to design data processing approaches, machine learning models, and experiments at scale. In addition, you will dive deeper into business intelligence aspects, informing our next steps, decisions, and investments in our business realm. You support the team in diving 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 statistical analysis, and you keep up to date with the latest scientific development in information processing, analytics, modelling, and learning methods.
· MSc or PhD degree in Computer Science or related field.
· For PhD: At least 2 years of data science/applied research experience working with industrial applications.
· For MSc: At least 4 years of data science/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 data science methods and machine learning: 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.
· Excellent writing and communication skills in English.
· Technical hands-on experience in general-purpose programming/scripting languages (e.g., Python, Scala, Java, or C++), modelling software (e.g. R or Matlab), and frameworks for large scale analytics (e.g. SparkML).
· Project leader and/or team lead experience.
· 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.
· Scientific publication experience in conferences and/or journals.