Applied Scientist, BRP Payment Risk ML, BRP Payment Risk ML

Saved job description

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud?

Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems?

Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment?

If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.

Key job responsibilities

Use machine learning and statistical techniques to create scalable risk management systems

Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends

Design, development and evaluation of highly innovative models for risk management

Working closely with software engineering teams to drive real-time model implementations and new feature creations

Working closely with operations staff to optimize risk management operations,

Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation

Tracking general business activity and providing clear, compelling management reporting on a regular basis

Research and implement novel machine learning and statistical approaches

Requirements: - 3+ years of building models for business application experience

- PhD, or Master's degree and 4+ 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