Amazon Applied Scientist Amazon B2B Paymants Lending Credit Science 

United States, Washington, Seattle 

347911629

ABPL is seeking an Applied Scientist who combines their scientific and technical expertise with business intuition to build flexible, performant, and global solutions for complex financial and risk problems. You will develop and deploy production models to enhance our product features & processes that will delight our customers.Key job responsibilities
- Apply advanced data mining, machine learning and other analytical/scientific techniques to create ML models and support Credit Management processes
- Source, incorporate, and analyze alternative credit data to drive innovation
- Own production model (real time and batch) , conduct code review and model monitoring to insist high bar of operating efficiencies and excellence and ensure high performant on the models- You will be responsible for researching as well as educating the business, product, marketing and product teams on the implementation of the models and enable strategic decision making.
A day in the life
As an Applied Scientist, you will design and build systems that support financial products. You will work closely with business partners, software and data engineers to build and deploy scalable solutions that deliver exceptional value for our customers. You will utilize intellectual and technical capabilities, problem solving and analytical skills, and excellent communication to deliver customer value. You will partner with product and operations management to launch new, or improve existing, financial products within Amazon.

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building machine learning models or developing algorithms for business application experience
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience programming in Java, C++, Python or related language
- Experience in patents or publications at top-tier peer-reviewed conferences or journals

- Experience implementing algorithms using both toolkits and self-developed code
- Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning