- Seattle, WA
Amazon Web Services (AWS) is the leading provider of cloud computing services in the world, offering a broad range of highly reliable, scalable, low-cost cloud services across over 190 countries. AWS infrastructure powers hundreds of thousands of enterprise, government and start-up businesses, including industry leaders such as Amazon.com, Netflix, Expedia, Airbnb, and many more, and renowned organizations such as NASA, the Centers for Disease Control and Prevention (CDC) and Coursera. Though already very successful AWS continues to be a high-growth, fast-moving division within Amazon with a start-up mentality where new and diverse challenges arise every day.
The AWS Central Economics team includes renowned experts in the area of forecasting, causal inference, and machine learning and applies economic theory and econometric analysis to build models, systems and tools that inform critical decisions for the AWS business. Such decisions include service valuation and pricing, infrastructure and hardware investments, sales and marketing investments, and impact the allocation of hundreds of millions of dollars.
We are looking for an Applied Scientist to explore and develop both supervised and unsupervised models from large and complex datasets. This person will work at the intersection of machine learning and economics, applying machine learning methods to answer cause-and-effect questions. The ideal candidate will have hands-on experience as well as be able to make the right decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate significant creativity and high judgment backed by statistical proof.
Key responsibilities include:
• Research, develop, and build supervised and unsupervised models
• Analyze and research features/data that help support these models
• Research and develop methods to explain supervised and unsupervised models
• Collaborate with other scientists and scholars to apply machine learning methods in econometric analyses
• Collaborate with data engineering and software engineering teams to design and implement end-to-end software solutions for science problems
• PhD or equivalent Master's Degree with 4+ years of experience in CS, CE, ML or related field
• 2+ years experience building machine learning models for business applications
• Experience programming in R and Python
• Experience with AWS technologies
• Experience working in a startup or early stage product environment and adjusting rapidly to evolving requirements and customer feedback
• Experience working effectively with science, data processing, and software engineering teams
• Background or specialization in unsupervised learning and semi-supervised learning
• Solid fundamentals in traditional machine learning and familiarity with deep learning approaches
• The ability to simplify problems by identifying new useful features or improve performance by feature engineering.
• Expertise in at least one more major programming language (C++, Java, or similar) and at least one scripting language (Python, or similar), and one Deep Learning Framework (MXNet, Tensor Flow, etc.).
• Excellent written and spoken communication skills.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
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