Applied Scientist, BRP Payment Risk ML

Description

n

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?

n

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?

n

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?

n

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.

n

Key job responsibilities

n

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

n

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

n

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

n

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

n

Working closely with operations staff to optimize risk management operations,

n

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

n

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

n

Research and implement novel machine learning and statistical approaches

n

Basic Qualifications

n

n
• n

  PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

  n
n
• n

  2+ years of building models for business application experience

  n
n
• n

  Experience programming in Java, C++, Python or related language

  n
n
• n

  Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

  n
n

n

Preferred Qualifications

n

n
• n

  A PhD in CS, Machine Learning, Statistics, Operations Research or relevant field

  n
n
• n

  5+ years of industry experience in predictive modeling and analysis

  n
n
• n

  Strong Machine Learning breadth and depth

  n
n
• n

  Strong skills with SQL Strong skills with Spark/Python/Perl (or similar)

  n
n
• n

  Ability to think creatively and solve problems

  n
n
• n

  Good written and spoken communication skills

  n
n
• n

  Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams

  n
n

n

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

n

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

n

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit . This position will remain posted until filled. Applicants should apply via our internal or external career site.