We are looking for a customer obsessed Applied Scientist who can apply the latest research, state of the art algorithms and machine learning to build highly scalable systems in the financial accounting domain. Does the challenge of advancing one of the world's most scalable, reliable, and secure e-commerce platforms that processes tens of millions of financial transactions, in multiple currencies and countries excite you? Have you ever wanted to work on machine learning problems that will make a lasting impact, solving key problems that impact the experience of millions of Amazon customers? If you are passionate about solving complex problems, in a challenging environment, we would love to talk with you.
As a member of our team you will develop and evaluate machine learning models using large data-sets, cloud services and customer behaviour to improve our customer's experience. Working closely with best-in-class engineers you will have the opportunity to apply a variety of machine learning algorithms, including deep learning, and work on one of the world's largest data sets to influence the long term evolution of our technology roadmap. You will need to be entrepreneurial, able to deal with ambiguity and work in a highly collaborative environment.
WHO IS FLASH?
Financial Ledger & Accounting Systems Hub (FLASH), a division within Amazon's eCommerce Foundation (eCF) is leading significant innovations in business systems integration and defining the future of accounting. Our group has been entrusted with accurate and timely recording of financial events for Amazon's revenue generating businesses and our systems are advancing one of the world's most scalable, reliable, and secure e-commerce platforms that process hundreds of billions of dollars in transactions, in multiple currencies and countries. We are at the centre of Amazon's key initiatives and fuelling the growth of Amazon's businesses worldwide. Think about what it takes to process the tens of millions of financial transactions that are generated each day as millions of purchases are made, as thousands of suppliers are paid, as inventory moves in and out of warehouses, and as vendors are paid!BASIC QUALIFICATIONS
• Ph.D./M.S. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
• 3+ years of hands-on experience in predictive modelling and analysis
• 2+ years hands-on experience in Python, 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 problemsPREFERRED QUALIFICATIONS
• 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;
• Experience working with Deep Learning frameworks (MxNet, TensorFlow, etc.);
• 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.
Engineering Information Technology