Amazon Snr Applied Scientist Tax Engine 

Canada, British Columbia, Vancouver 

534698888

We are responsible for the tax calculation platform, providing the core services that calculate taxes (sales tax and VAT) for all Amazon sales, physical and digital, globally. We seek to provide the correct tax amounts to the customer when placing their Amazon order, and ensure all records are stored safely to meet tax law requirements around the globe. Our challenges include staying on top of the complex and ever-changing global tax legislations as well as computing calculations correctly and quickly, thousands of times a second, and each one needs to be accurate.As an Applied scientist, you will provide machine learning leadership to the team that helps increase the accuracy of Tax classification based product information in Amazon catalogue making it the biggest and most challenging tax classification using Machine learning models globally. You will work with large language models, to build various machine learning models to predict accuracy of human's on specific tasks, reason with large volumes of systems changes to identify causal determinants, apply generative AI to model outcomes from sparse data.
You will help us innovate different ways to enhance tax classification experience for our global customers.
You will need to be entrepreneurial, work in a highly collaborative environment with SDEs, Product managers and businesses. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.

- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.