Come join a team to work in the intersection of Machine Learning, Observability, Cloud, Big Data and Open Source!
The AWS CloudWatch Predictions team produces Anomaly Detection solution that gives customers actionable visibility into the health of their applications and services by leveraging machine learning technologies. Our service continuously analyzes system and application metrics, detects and surfaces anomalies without requiring user intervention, enables AWS customers across the world to monitor and act on the dynamic nature of system and application behaviors.
We are looking for applied scientists to help us lend meaning to vast amounts of time series data and delight our customers by finding the reasonable answers to the right problems. If you enjoyed your studies of Time Series Modeling and Predicting, Anomaly Detection Algorithms, Data Smoothing, Outlier Filtering, and innovating new features that can make a huge impact on the customer experience excites you, then we've got a good home for you here.
You'll have a ground floor opportunity to work on cutting edge ways for online time series forecasting and anomaly detection, and improve the the use of advanced Machine Learning on massive scale datasets. You'll join a team of veteran service engineers who are focused on highly stable, low operational burden, continuously deployed software and help lead a new quantitative effort. It'll be an opportunity to not just change how the world understands their compute resources, but the opportunity to build world class distributed system software engineering skills as well.
· PhD or equivalent Master's Degree plus 4+ years of experience in Mathematics, Statistics, or a related quantitative field with an emphasis on numerical methods and time series analysis
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· Strong Computer Science fundamentals in data structures, algorithm design, problem solving, and complexity analysis
· Fluency in written and spoken English
· More than 4 years of industrial/academic experience in time series modeling and predicting
· Ability to handle multiple competing priorities in a fast-paced environment
· Significant peer reviewed scientific contributions in premier journals and conferences
· Strong personal interest in learning, researching, and creating new technologies with high customer impact
· Experience with defining research and development practices in an applied environment;
· Proven track record in technically leading and mentoring scientists;
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
· Strong fundamentals in problem solving, algorithm design and complexity analysis