Applied Scientist — WW Installments ML for Payments

Amazon Inc.; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

RSUs (Restricted Stock Units)

401(k) matching

Job summary

Amazon Inc. WW Installments is seeking an Applied Scientist to join a team building machine learning models and pipelines with direct customer impact. You will work on production systems that influence financing options for Amazon customers, improving the shopping experience for hundreds of millions worldwide.

You will collaborate with data engineers, software developers, and business partners to induce data-driven decisions and deliver analytics roadmaps with autonomy in a complex environment.

Qualifications

• Master's degree or higher in computer science, machine learning, engineering, or related field.
• 3+ years of building models for business applications.

Responsibilities

• Develop production ML models and pipelines for the WW Installments Fraud Detection team with direct customer impact.
• Apply ML to large-scale production systems deployed across Amazon businesses.
• Identify opportunities, define modeling approaches, and deliver outcomes to Amazon businesses with an Amazon-wide perspective.
• Lead production ML implementation including addressing consumer financial credit and fraud risks.
• Collaborate with engineers, scientists, and stakeholders to deliver ML pipelines and analytics projects.

Skills

Machine learning

Java

C++

Python

Data mining

Algorithms

Education

Master's degree or higher in CS/ML/Engineering

PhD in CS/ML/Engineering

Tools

Unix/Linux

Job description

Amazon Inc. WW Installments is seeking an Applied Scientist to join a team building machine learning models and pipelines with direct customer impact. You will work on production systems that influence financing options for Amazon customers, improving the shopping experience for hundreds of millions worldwide.

You will collaborate with data engineers, software developers, and business partners to induce data-driven decisions and deliver analytics roadmaps with autonomy in a complex environment.