Would you like to work on one of the world's largest transactional distributed systems? How about working with customers and peers from the entire range of Amazon's business on cool new features? Whether you're passionate about building highly scalable and reliable systems or a scientist who likes to solve business problems, Amazon Tax Platform Services is the place for you.
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 thrive on providing the correct tax amounts to the customer at order time, and make sure audit 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 rates and laws as well as computing calculations correctly and quickly, thousands of times a second, and each one needs to be right.
As an Applied scientist, you will provide machine learning leadership to the team that helps increase the accuracy of Tax classification for overall Amazon catalogue making it the biggest and most challenging tax classification using Machine learning models globally. You will build various data and machine learning models that help us innovate different ways to enhance tax classification experience.
You will need to be entrepreneurial, wear many hats, and work in a highly collaborative environment. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.
· Ph.D./M.S. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· 5+ years of hands-on experience in predictive modeling and analysis
· 2+ years hands-on experience in Python, Perl, Scala, Java, C#, C++ or other similar languages
· 1+ years professional experience in software development
· Proficiency in model development, model validation and model implementation for large-scale applications
· Ability to convey mathematical results to non-science stakeholders
Strength in clarifying and formalizing complex problems
· Ph.D. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· 6+ years of practical experience applying ML to solve complex problems in an applied environment
· Significant peer-reviewed scientific contributions in premier journals and conferences
· Strong CS fundamentals in data structures, 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