Sr. Applied Scientist, Amazon B2B Payments and Lending, Credit Science

Amazon; New York (NY); On-site; USD 90,000 - 160,000; Full time

Job summary

An established industry player is seeking a Senior Applied Scientist to leverage their expertise in machine learning and data analysis to tackle complex financial challenges. This role involves designing innovative systems for financial products, collaborating with cross-functional teams, and deploying machine learning models that drive customer success. Join a dynamic team where your contributions will directly impact B2B payments and lending solutions. If you're passionate about applying your skills in a fast-paced environment and making a global impact, this opportunity is perfect for you.

Benefits

Medical, Dental, and Vision Coverage

Maternity and Parental Leave Options

Paid Time Off (PTO)

401(k) Plan

Qualifications

• 5+ years experience in a science role, applying ML to solve complex problems.
• Excellent communication and collaboration skills required.

Responsibilities

• Design and build systems that support financial products.
• Apply advanced data mining and machine learning techniques.

Skills

Machine Learning

Python

SQL

Analytical Skills

Problem Solving

Communication Skills

Education

Master's Degree in a quantitative field

Ph.D in a quantitative field

Tools

AWS

Job description

Sr. Applied Scientist, Amazon B2B Payments and Lending, Credit Science

Job ID: 2921159 | Amazon.com Services LLC

If you are excited about applying your science and engineering skills in business problems in the space of Credit management, B2B Financial Service, and Payments, we invite you to consider this Applied Scientist opportunity within Amazon B2B Payments and Lending (ABPL).

ABPL is seeking a Senior 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:

• As a Sr. 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.

Other responsibilities include:

• 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 performance 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:
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:

1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

BASIC QUALIFICATIONS

- Master's Degree in a quantitative field (Statistics, Mathematics, Computer Science, Machine Learning or equivalent)
- 5+ years experience in a science role, applying ML to solve complex problems for large-scale applications
- Superior analytical skills. Demonstrated ability to identify and solve ambiguous problems
- Demonstrated attention to detail and desire to roll up your sleeves
- Demonstrated ability to operate both strategically and tactically in a high-energy, fast-paced environment. High degree of organization and ability to manage multiple, competing priorities.
- Excellent communication (verbal and written) and collaboration skills that enable you to earn trust at all levels
- Proficiency in Python, SQL, or other programming language
- Experience developing machine learning solutions with AWS

PREFERRED QUALIFICATIONS

- Ph.D in a quantitative field (Statistics, Mathematics, Computer Science, Machine Learning or Equivalent)
- 10+ years of practical experience applying ML to solve complex problems
- 3+ years experience in credit underwriting or related work in financial services domain
- Project management experience for working on cross-functional projects
- Experience in developing and applying Large Language Models (LLM) to solve complex problems for large-scale applications
- Successful record of developing junior members from academia/industry
- Proven achievements of developing and managing a long-term research vision and portfolio of research initiatives, with algorithms and models that have been successfully integrated in production systems

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.