Applied Scientist, Installments

Position: Applied Scientist, Worldwide Installments
Description

WW Installments is one of the fastest growing businesses within Amazon and we are looking for an Applied Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits  We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way.

Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.

As an Applied Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.

Key job responsibilities

This role will be responsible for:

- Developing production machine learning models and pipelines for the WW Installments Fraud Detection team that directly impact customers.

- Apply expertise in machine learning to develop large-scale production systems that are deployed across Amazon businesses.

- Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.

- Lead the implementation of production ML from a scientific perspective including identifying consumer financial credit and fraud risks, key milestones, and paths to mitigate risks.

- Identifying new opportunities to influence business strategy and product vision using data science and machine learning.

- Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.

- Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.

- Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.

Basic Qualifications

- Master's degree or above in computer science, machine learning, engineering, or related fields

- 3+ years of building models for business application experience

- Experience in patents or publications at top-tier peer-reviewed conferences or journals

- 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

Preferred Qualifications

- PhD in computer science, machine learning, engineering, or related fields

- 5+ years of building machine learning models or developing algorithms for business application experience

- Experience using Unix/Linux

- Experience in professional software development

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

Learn more about our benefits at  (Use the "Apply for this Job" box below). .

USA, VA, Arlington -  -  USD annually

USA, WA, Seattle -  -  USD annually