Applied Scientist

Job description

DESCRIPTION

Do you enjoy building flexible, performant, and global solutions for complex financial and risk problems? We are looking for scientists to help us improve the customer experience in the financial industry!

As a Scientist on the Amazon Lending team, you will design and build systems that support financial products. You will work closely with software and data engineers to build 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.

Amazon Lending is an exciting program designed to help provide our selling partners with funds to grow their businesses. We take pride in building solutions that leverage key insights to help our selling partners to be successful. We are looking for a talented and passionate scientists that can develop a reliable, scalable, and technical infrastructure to serve customer needs and improve portfolio performance as we expand the program.

PREFERRED QUALIFICATIONS

· Applicants with considerably more experience, including mid-career, are also strongly encouraged.
· Experience using AWS technologies.
· Strong background in statistics methodology, applications to business problems, and big data.
· Large breadth of experience with of predictive modeling using supervised or unsupervised learning techniques.
· Ability to work in a fast-paced business environment.
· Effective verbal and written communications skills.
· Credit or risk management experience.

Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

Desired profile

BASIC QUALIFICATIONS

· Graduate degree in Statistics, Economics,Engineering, Computer Science, Mathematics, or related technical field
· Experience using predictive models to make business decisions.
· Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements