• 3 years of building models for business application experience
• PhD, or Master's degree and 4 years of CS, CE, ML or related field experience
• Experience building machine learning models or developing algorithms for business application
• Experience programming in Java, C , Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

If you are excited about applying your science and engineering skills in business problems in the space of risk measurement, quantification, and mitigation, we invite you to consider this Applied Scientist opportunity within Amazon B2B Payment and Lending (ABPL).
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
• Collaborate effectively with Credit Strategy, Operations, Product, data and engineering teams in ABPL to underwrite new customers and manage portfolio risk
• 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.
• Understand business and product strategies, goals and objectives. Make recommendations for new techniques/strategies to improve customer outcomes.

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.
We are open to hiring candidates to work out of one of the following locations:
Austin, TX, USA | New York City, NY, USA | Seattle, WA, USA

• Experience implementing algorithms using both toolkits and self-developed code
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.