In the Denied Party Screening group we build automatic mechanisms to detect and prevent prohibited transactions with denied parties. We are looking for an Applied Scientist with strong analytical and problem solving skills, who will participate in full development cycle for a mission critical system that screens customers from the moment they are introduced to Amazon and at every point they interact and transact with Amazon’s various businesses and products. The project is continuously growing and creating opportunities to innovate and deliver.
You will collaborate with the leadership team to drive key engineering and business decisions that will influence the Amazon transaction compliance. You will use Amazon’s large-scale computing resources to build models and you will work with domain experts and engineers to help turn those models into production solutions. You will participate in the Amazon ML community and mentor software development engineers with a strong interest in and knowledge of ML.
We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day by all Amazon businesses.
· 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.
· 5+ years of 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.