Applied Scientist II, FinAuto

About the Role

Interested in building the next-generation Financial systems that can handle billions of dollars in transactions? Interested in building highly scalable next-generation systems that could utilize Amazon Cloud? We face massive data volume + complex business rules in a highly distributed and service-oriented architecture, presenting a world-class information collection and delivery challenge. Our goal is to deliver software systems which accurately capture, process, and report on the huge volume of financial transactions generated each day as millions of customers make purchases, thousands of Vendors and Partners are paid, inventory moves, commissions are calculated, and taxes are collected in hundreds of jurisdictions worldwide.

The FinAuto TFAW (theft, fraud, abuse, waste) team is part of the FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive, and wasteful (TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conducting research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinating with the Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

Key Job Responsibilities

• Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference.
• Analyze and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes.
• Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection.
• Use machine learning and analytical techniques to create scalable solutions for business problems.
• Identify new areas where machine learning can be applied for solving business problems.
• Partner with developers and business teams to put your models in production.
• Mentor other scientists and engineers in the use of ML techniques.

Basic Qualifications

• PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience.
• 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

• Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms.
• Experience in professional software development.