The Consumer Cloud Security (C2S) group is responsible for the protection of customer and corporate data. We are connected to all parts of Amazon's business and it’s massive, worldwide service-oriented architecture. We are starting the work on a new mission critical system that will preserve and improve the trusted experience that Amazon provides to its customers. This is a greenfield initiative with plenty of opportunity for innovation in the security space through new machine learning techniques.
We are seeking a talented, self-directed Applied Scientist to work on the cutting edge security technologies. You'll design and run experiments, research new algorithms, and find new ways of protecting Amazon's customer trust. Besides theoretical analysis and innovation, you will work closely with talented engineers to put your algorithms and models into practice. You should thrive in ambiguous environments that require to find solutions to problems that have not been solved before. You enjoy and succeed in fast paced environments where learning new concepts quickly is a must. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience in building large-scale distributed systems. Your strong communication skills enable you to work effectively with both business and technical partners.
· Process and analyze large data sets using as many techniques as necessary
· Deliver scalable models that can analyze large data sets efficiently
· Build mathematical models to detect and classify specific data elements with high accuracy
· Prototype these models by using high-level modeling languages such as R or in software languages such as Python. A software team will be working with you to transform prototypes into production.
· Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.
· M.S. or PhD in Computer Science, Machine Learning, Operational Research, Statistics, or a other quantitative field;
· 3+ years of practical experience applying ML to solve complex problems;
· Algorithm and model development experience for large-scale applications;
· Experience using Java, C++, or other programming language, as well as with R or Python;
· Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.
· Practical experience applying ML to solve complex problems;
· Significant peer reviewed scientific contributions in premier journals and conferences;
· Strong fundamentals in problem solving, algorithm design and complexity analysis;
· 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.